


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 				A       (			unittest.TestCase				):
					'''simple docstring'''






					def     lowerCamelCase__	(self       :		int						)		->  Union[str, Any]:




							"""simple docstring"""


							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()

					@property
					def     lowerCamelCase__	(self       :		Dict						)		->  List[Any]:




							"""simple docstring"""


							torch.manual_seed(0						)
							lowercase__	     =						UNetaDModel(
							    sample_size=(32, 64) ,   in_channels=1 ,   out_channels=1 ,   layers_per_block=2 ,   block_out_channels=(128, 128) ,   down_block_types=("""AttnDownBlock2D""", """DownBlock2D""") ,   up_block_types=("""UpBlock2D""", """AttnUpBlock2D""") ,   )
							return model

					@property
					def     lowerCamelCase__	(self       :		List[Any]						)		->  List[str]:




							"""simple docstring"""


							torch.manual_seed(0						)
							lowercase__	     =						UNetaDConditionModel(
							    sample_size=(64, 32) ,   in_channels=1 ,   out_channels=1 ,   layers_per_block=2 ,   block_out_channels=(128, 128) ,   down_block_types=("""CrossAttnDownBlock2D""", """DownBlock2D""") ,   up_block_types=("""UpBlock2D""", """CrossAttnUpBlock2D""") ,   cross_attention_dim=10 ,   )
							return model

					@property
					def     lowerCamelCase__	(self       :		Dict						)		->  Any:




							"""simple docstring"""


							torch.manual_seed(0						)
							lowercase__	     =						AutoencoderKL(
							    sample_size=(128, 64) ,   in_channels=1 ,   out_channels=1 ,   latent_channels=1 ,   layers_per_block=2 ,   block_out_channels=(128, 128) ,   down_block_types=("""DownEncoderBlock2D""", """DownEncoderBlock2D""") ,   up_block_types=("""UpDecoderBlock2D""", """UpDecoderBlock2D""") ,   )
							lowercase__	     =						UNetaDModel(
							    sample_size=(64, 32) ,   in_channels=1 ,   out_channels=1 ,   layers_per_block=2 ,   block_out_channels=(128, 128) ,   down_block_types=("""AttnDownBlock2D""", """DownBlock2D""") ,   up_block_types=("""UpBlock2D""", """AttnUpBlock2D""") ,   )
							return vqvae, unet


					@slow
					def     lowerCamelCase__	(self       :		Optional[int]						)		->  Any:




							"""simple docstring"""


							lowercase__	     =						"""cpu"""  # ensure determinism for the device-dependent torch.Generator
							lowercase__	     =						Mel(
							    x_res=self.dummy_unet.config.sample_size[1] ,   y_res=self.dummy_unet.config.sample_size[0] ,   )

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

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

							lowercase__	     =						torch.Generator(device=_UpperCAmelCase						).manual_seed(42						)
							lowercase__	     =						pipe(generator=_UpperCAmelCase ,   steps=4 ,   return_dict=_UpperCAmelCase						)
							lowercase__	     =						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]
							)
							lowercase__	     =						np.frombuffer(image.tobytes() ,   dtype="""uint8"""						)[:10]
							lowercase__	     =						np.frombuffer(image_from_tuple.tobytes() ,   dtype="""uint8"""						)[:10]
							lowercase__	     =						np.array([69, 255, 255, 255, 0, 0, 77, 181, 12, 127]						)

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

							lowercase__	     =						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] ,   )

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

							np.random.seed(0						)
							lowercase__	     =						np.random.uniform(-1 ,   1 ,   ((dummy_vqvae_and_unet[0].config.sample_size[1] - 1) * mel.hop_length,)						)
							lowercase__	     =						torch.Generator(device=_UpperCAmelCase						).manual_seed(42						)
							lowercase__	     =						pipe(raw_audio=_UpperCAmelCase ,   generator=_UpperCAmelCase ,   start_step=5 ,   steps=10						)
							lowercase__	     =						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]
							)
							lowercase__	     =						np.frombuffer(image.tobytes() ,   dtype="""uint8"""						)[:10]
							lowercase__	     =						np.array([120, 117, 110, 109, 138, 167, 138, 148, 132, 121]						)

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

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

							np.random.seed(0						)
							lowercase__	     =						torch.rand((1, 1, 10)						)
							lowercase__	     =						pipe(generator=_UpperCAmelCase ,   encoding=_UpperCAmelCase						)
							lowercase__	     =						output.images[0]
							lowercase__	     =						np.frombuffer(image.tobytes() ,   dtype="""uint8"""						)[:10]
							lowercase__	     =						np.array([107, 103, 120, 127, 142, 122, 113, 122, 97, 111]						)

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






@slow
@require_torch_gpu
class 				A       (			unittest.TestCase				):
					'''simple docstring'''






					def     lowerCamelCase__	(self       :		Dict						)		->  List[str]:




							"""simple docstring"""


							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()


					def     lowerCamelCase__	(self       :		str						)		->  List[str]:




							"""simple docstring"""


							lowercase__	     =						torch_device

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

							lowercase__	     =						torch.Generator(device=_UpperCAmelCase						).manual_seed(42						)
							lowercase__	     =						pipe(generator=_UpperCAmelCase						)
							lowercase__	     =						output.audios[0]
							lowercase__	     =						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]
							lowercase__	     =						np.frombuffer(image.tobytes() ,   dtype="""uint8"""						)[:10]
							lowercase__	     =						np.array([151, 167, 154, 144, 122, 134, 121, 105, 70, 26]						)

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





from __future__ import annotations

from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any



@dataclass
class 				A       :
					'''simple docstring'''






					A__     =					42
					A__     =					None
					A__     =					None






def 					UpperCamelCase   (      )  ->    Node | None:
		"""simple docstring"""




		lowercase__	     =						Node(1	)
		lowercase__	     =						Node(2	)
		lowercase__	     =						Node(3	)
		lowercase__	     =						Node(4	)
		lowercase__	     =						Node(5	)
		return tree






def 					UpperCamelCase   (      __magic_name__	:				Node | None	)  ->    list[int]:
		"""simple docstring"""




		return [root.data, *preorder(root.left	), *preorder(root.right	)] if root else []






def 					UpperCamelCase   (      __magic_name__	:				Node | None	)  ->    list[int]:
		"""simple docstring"""




		return postorder(root.left	) + postorder(root.right	) + [root.data] if root else []






def 					UpperCamelCase   (      __magic_name__	:				Node | None	)  ->    list[int]:
		"""simple docstring"""




		return [*inorder(root.left	), root.data, *inorder(root.right	)] if root else []






def 					UpperCamelCase   (      __magic_name__	:				Node | None	)  ->    int:
		"""simple docstring"""




		return (max(height(root.left	)       ,       height(root.right	)	) + 1) if root else 0






def 					UpperCamelCase   (      __magic_name__	:				Node | None	)  ->    Sequence[Node | None]:
		"""simple docstring"""




		lowercase__	     =						[]

		if root is None:
				return output

		lowercase__	     =						deque([root]	)

		while process_queue:
				lowercase__	     =						process_queue.popleft()
				output.append(node.data	)

				if node.left:
						process_queue.append(node.left	)
				if node.right:
						process_queue.append(node.right	)
		return output






def 					UpperCamelCase   (      __magic_name__	:				Node | None       ,       __magic_name__	:				int	)  ->    Sequence[Node | None]:
		"""simple docstring"""




		lowercase__	     =						[]

		def populate_output(__magic_name__	:				Node | None       ,       __magic_name__	:				int	) -> None:
				if not root:
						return
				if level == 1:
						output.append(root.data	)
				elif level > 1:
						populate_output(root.left       ,       level - 1	)
						populate_output(root.right       ,       level - 1	)

		populate_output(__magic_name__       ,       __magic_name__	)
		return output






def 					UpperCamelCase   (      __magic_name__	:				Node | None       ,       __magic_name__	:				int	)  ->    Sequence[Node | None]:
		"""simple docstring"""




		lowercase__	     =						[]

		def populate_output(__magic_name__	:				Node | None       ,       __magic_name__	:				int	) -> None:
				if root is None:
						return
				if level == 1:
						output.append(root.data	)
				elif level > 1:
						populate_output(root.right       ,       level - 1	)
						populate_output(root.left       ,       level - 1	)

		populate_output(__magic_name__       ,       __magic_name__	)
		return output






def 					UpperCamelCase   (      __magic_name__	:				Node | None	)  ->    Sequence[Node | None] | list[Any]:
		"""simple docstring"""




		if root is None:
				return []

		lowercase__	     =						[]

		lowercase__	     =						0
		lowercase__	     =						height(__magic_name__	)

		for h in range(1       ,       height_tree + 1	):
				if not flag:
						output.append(get_nodes_from_left_to_right(__magic_name__       ,       __magic_name__	)	)
						lowercase__	     =						1
				else:
						output.append(get_nodes_from_right_to_left(__magic_name__       ,       __magic_name__	)	)
						lowercase__	     =						0

		return output






def 					UpperCamelCase   (      )  ->    None:  # Main function for testing.
		"""simple docstring"""




		lowercase__	     =						make_tree()
		print(f'''In-order Traversal: {inorder(__magic_name__	)}'''	)
		print(f'''Pre-order Traversal: {preorder(__magic_name__	)}'''	)
		print(f'''Post-order Traversal: {postorder(__magic_name__	)}'''       ,       """\n"""	)

		print(f'''Height of Tree: {height(__magic_name__	)}'''       ,       """\n"""	)

		print("""Complete Level Order Traversal: """	)
		print(level_order(__magic_name__	)       ,       """\n"""	)

		print("""Level-wise order Traversal: """	)

		for level in range(1       ,       height(__magic_name__	) + 1	):
				print(f'''Level {level}:'''       ,       get_nodes_from_left_to_right(__magic_name__       ,       level=__magic_name__	)	)

		print("""\nZigZag order Traversal: """	)
		print(zigzag(__magic_name__	)	)


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	main()

