





def 		_SCREAMING_SNAKE_CASE    (   lowerCAmelCase__       ,lowerCAmelCase__	):






		if not root or root not in binary_tree_mirror_dictionary:
				return
		lowerCamelCase_  :    List[Any]   	=			binary_tree_mirror_dictionary[root][:2]
		lowerCamelCase_  :    Tuple   	=			[right_child, left_child]
		binary_tree_mirror_dict(UpperCAmelCase_       ,UpperCAmelCase_	)
		binary_tree_mirror_dict(UpperCAmelCase_       ,UpperCAmelCase_	)


def 		_SCREAMING_SNAKE_CASE    (   lowerCAmelCase__       ,lowerCAmelCase__ = 1	):






		if not binary_tree:
				raise ValueError('binary tree cannot be empty'	)
		if root not in binary_tree:
				lowerCamelCase_  :    List[str]   	=			F"root {root} is not present in the binary_tree"
				raise ValueError(UpperCAmelCase_	)
		lowerCamelCase_  :    Dict   	=			dict(UpperCAmelCase_	)
		binary_tree_mirror_dict(UpperCAmelCase_       ,UpperCAmelCase_	)
		return binary_tree_mirror_dictionary


if __name__ == "__main__":
		_lowercase		:     int 					={1: [2, 3], 2: [4, 5], 3: [6, 7], 7: [8, 9]}
		print(F'''Binary tree: {binary_tree}''')
		_lowercase		:     Dict 					=binary_tree_mirror(binary_tree, 5)
		print(F'''Binary tree mirror: {binary_tree_mirror_dictionary}''')







import gc
import unittest

import numpy as np
import torch

from diffusers import DDPMScheduler, MidiProcessor, SpectrogramDiffusionPipeline
from diffusers.pipelines.spectrogram_diffusion import SpectrogramContEncoder, SpectrogramNotesEncoder, TaFilmDecoder
from diffusers.utils import require_torch_gpu, skip_mps, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_note_seq, require_onnxruntime

from ..pipeline_params import TOKENS_TO_AUDIO_GENERATION_BATCH_PARAMS, TOKENS_TO_AUDIO_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()


_lowercase		:     Optional[Any] 					="""./tests/fixtures/elise_format0.mid"""






@unittest.skip('The note-seq package currently throws an error on import'       )
class 	UpperCamelCase_     (     snake_case__     ,    unittest.TestCase       ):
					_a			:	int							=				SpectrogramDiffusionPipeline
					_a			:	Dict							=				PipelineTesterMixin.required_optional_params - {
					    'callback',
					    'latents',
					    'callback_steps',
					    'output_type',
					    'num_images_per_prompt',
					}
					_a			:	str							=				False
					_a			:	Union[str, Any]							=				False
					_a			:	Optional[int]							=				TOKENS_TO_AUDIO_GENERATION_PARAMS
					_a			:	Optional[Any]							=				TOKENS_TO_AUDIO_GENERATION_BATCH_PARAMS



					def      __a    (		self    :    Dict	):
							torch.manual_seed(0	)
							lowerCamelCase_  :    List[str]   	=			SpectrogramNotesEncoder(
							    max_length=20_48					,			vocab_size=15_36					,			d_model=7_68					,			dropout_rate=0.1					,			num_layers=1					,			num_heads=1					,			d_kv=4					,			d_ff=20_48					,			feed_forward_proj='gated-gelu'					,			)

							lowerCamelCase_  :    Union[str, Any]   	=			SpectrogramContEncoder(
							    input_dims=1_28					,			targets_context_length=2_56					,			d_model=7_68					,			dropout_rate=0.1					,			num_layers=1					,			num_heads=1					,			d_kv=4					,			d_ff=20_48					,			feed_forward_proj='gated-gelu'					,			)

							lowerCamelCase_  :    Union[str, Any]   	=			TaFilmDecoder(
							    input_dims=1_28					,			targets_length=2_56					,			max_decoder_noise_time=20_000.0					,			d_model=7_68					,			num_layers=1					,			num_heads=1					,			d_kv=4					,			d_ff=20_48					,			dropout_rate=0.1					,			)

							lowerCamelCase_  :    Any   	=			DDPMScheduler()

							lowerCamelCase_  :    str   	=			{
							    'notes_encoder': notes_encoder.eval(),
							    'continuous_encoder': continuous_encoder.eval(),
							    'decoder': decoder.eval(),
							    'scheduler': scheduler,
							    'melgan': None,
							}
							return components



					def      __a    (		self    :    Any					,			lowerCamelCase    :    Any					,			lowerCamelCase    :    int=0	):
							if str(lowerCamelCase	).startswith('mps'	):
									lowerCamelCase_  :    Optional[int]   	=			torch.manual_seed(lowerCamelCase	)
							else:
									lowerCamelCase_  :    Dict   	=			torch.Generator(device=lowerCamelCase	).manual_seed(lowerCamelCase	)
							lowerCamelCase_  :    str   	=			{
							    'input_tokens': [
							        [11_34, 90, 11_35, 11_33, 10_80, 1_12, 11_32, 10_80, 11_33, 10_79, 1_33, 11_32, 10_79, 11_33, 1] + [0] * 20_33
							    ],
							    'generator': generator,
							    'num_inference_steps': 4,
							    'output_type': 'mel',
							}
							return inputs



					def      __a    (		self    :    Dict	):
							lowerCamelCase_  :    str   	=			'cpu'  # ensure determinism for the device-dependent torch.Generator
							lowerCamelCase_  :    Dict   	=			self.get_dummy_components()
							lowerCamelCase_  :    Tuple   	=			SpectrogramDiffusionPipeline(**lowerCamelCase	)
							lowerCamelCase_  :    List[str]   	=			pipe.to(lowerCamelCase	)
							pipe.set_progress_bar_config(disable=lowerCamelCase	)

							lowerCamelCase_  :    Optional[Any]   	=			self.get_dummy_inputs(lowerCamelCase	)
							lowerCamelCase_  :    str   	=			pipe(**lowerCamelCase	)
							lowerCamelCase_  :    Tuple   	=			output.audios

							lowerCamelCase_  :    Tuple   	=			mel[0, -3:, -3:]

							assert mel_slice.shape == (3, 3)
							lowerCamelCase_  :    List[str]   	=			np.array(
							    [-11.512_925, -4.788_215, -0.46_172_905, -2.051_715, -10.539_147, -10.970_963, -9.091_634, 4.0, 4.0]	)
							assert np.abs(mel_slice.flatten() - expected_slice	).max() < 1E-2



					@skip_mps
					def      __a    (		self    :    Any	):
							return super().test_save_load_local()



					@skip_mps
					def      __a    (		self    :    Tuple	):
							return super().test_dict_tuple_outputs_equivalent()



					@skip_mps
					def      __a    (		self    :    Dict	):
							return super().test_save_load_optional_components()



					@skip_mps
					def      __a    (		self    :    str	):
							return super().test_attention_slicing_forward_pass()



					def      __a    (		self    :    Any	):
							pass



					def      __a    (		self    :    Optional[int]	):
							pass





					@skip_mps
					def      __a    (		self    :    Optional[Any]	):
							return super().test_progress_bar()






@slow
@require_torch_gpu
@require_onnxruntime
@require_note_seq
class 	UpperCamelCase_     (     unittest.TestCase       ):



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



					def      __a    (		self    :    str	):
							# TODO - test that pipeline can decode tokens in a callback
							# so that music can be played live
							lowerCamelCase_  :    Optional[Any]   	=			torch_device

							lowerCamelCase_  :    Union[str, Any]   	=			SpectrogramDiffusionPipeline.from_pretrained('google/music-spectrogram-diffusion'	)
							lowerCamelCase_  :    str   	=			pipe.melgan
							lowerCamelCase_  :    Optional[int]   	=			None

							lowerCamelCase_  :    str   	=			pipe.to(lowerCamelCase	)
							pipe.set_progress_bar_config(disable=lowerCamelCase	)

							def callback(lowerCamelCase    :    int					,			lowerCamelCase    :    int	):
									# decode mel to audio
									lowerCamelCase_  :    Union[str, Any]   	=			melgan(input_features=mel_output.astype(np.floataa	)	)[0]
									assert len(audio[0]	) == 8_19_20 * (step + 1)
									# simulate that audio is played
									return audio

							lowerCamelCase_  :    Any   	=			MidiProcessor()
							lowerCamelCase_  :    str   	=			processor(lowerCamelCase	)

							lowerCamelCase_  :    str   	=			input_tokens[:3]
							lowerCamelCase_  :    Dict   	=			torch.manual_seed(0	)
							pipe(lowerCamelCase					,			num_inference_steps=5					,			generator=lowerCamelCase					,			callback=lowerCamelCase					,			output_type='mel'	)



					def      __a    (		self    :    str	):
							lowerCamelCase_  :    List[str]   	=			torch_device

							lowerCamelCase_  :    List[str]   	=			SpectrogramDiffusionPipeline.from_pretrained('google/music-spectrogram-diffusion'	)
							lowerCamelCase_  :    Union[str, Any]   	=			pipe.to(lowerCamelCase	)
							pipe.set_progress_bar_config(disable=lowerCamelCase	)
							lowerCamelCase_  :    int   	=			MidiProcessor()

							lowerCamelCase_  :    Dict   	=			processor(lowerCamelCase	)
							# just run two denoising loops
							lowerCamelCase_  :    Optional[Any]   	=			input_tokens[:2]

							lowerCamelCase_  :    List[Any]   	=			torch.manual_seed(0	)
							lowerCamelCase_  :    str   	=			pipe(lowerCamelCase					,			num_inference_steps=2					,			generator=lowerCamelCase	)

							lowerCamelCase_  :    Dict   	=			output.audios[0]

							assert abs(np.abs(lowerCamelCase	).sum() - 3_612.841	) < 1E-1





					def      __a    (		self    :    List[Any]	):
							lowerCamelCase_  :    int   	=			torch_device

							lowerCamelCase_  :    str   	=			SpectrogramDiffusionPipeline.from_pretrained('google/music-spectrogram-diffusion'	)
							lowerCamelCase_  :    Optional[int]   	=			pipe.to(lowerCamelCase	)
							pipe.set_progress_bar_config(disable=lowerCamelCase	)

							lowerCamelCase_  :    Tuple   	=			MidiProcessor()

							lowerCamelCase_  :    int   	=			processor(lowerCamelCase	)

							# just run 4 denoising loops
							lowerCamelCase_  :    Optional[Any]   	=			input_tokens[:4]

							lowerCamelCase_  :    List[Any]   	=			torch.manual_seed(0	)
							lowerCamelCase_  :    int   	=			pipe(lowerCamelCase					,			num_inference_steps=1_00					,			generator=lowerCamelCase	)

							lowerCamelCase_  :    List[Any]   	=			output.audios[0]
							assert abs(np.abs(lowerCamelCase	).sum() - 9_389.1_111	) < 5E-2
