



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()


_A       =					"./tests/fixtures/elise_format0.mid"





@unittest.skip('''The note-seq package currently throws an error on import'''	)
class      _lowerCAmelCase (     lowerCAmelCase_ ,				unittest.TestCase	):
				_lowercase      		=SpectrogramDiffusionPipeline
				_lowercase      		=PipelineTesterMixin.required_optional_params - {
				    '''callback''',
				    '''latents''',
				    '''callback_steps''',
				    '''output_type''',
				    '''num_images_per_prompt''',
				}
				_lowercase      		=False
				_lowercase      		=False
				_lowercase      		=TOKENS_TO_AUDIO_GENERATION_PARAMS
				_lowercase      		=TOKENS_TO_AUDIO_GENERATION_BATCH_PARAMS




				def 					__a						( self					)    ->				int:
							torch.manual_seed(0					)
							lowerCAmelCase_      =     SpectrogramNotesEncoder(
							    max_length=2_048    ,    vocab_size=1_536    ,    d_model=768    ,    dropout_rate=0.1    ,    num_layers=1    ,    num_heads=1    ,    d_kv=4    ,    d_ff=2_048    ,    feed_forward_proj="gated-gelu"    ,    )

							lowerCAmelCase_      =     SpectrogramContEncoder(
							    input_dims=128    ,    targets_context_length=256    ,    d_model=768    ,    dropout_rate=0.1    ,    num_layers=1    ,    num_heads=1    ,    d_kv=4    ,    d_ff=2_048    ,    feed_forward_proj="gated-gelu"    ,    )

							lowerCAmelCase_      =     TaFilmDecoder(
							    input_dims=128    ,    targets_length=256    ,    max_decoder_noise_time=20_000.0    ,    d_model=768    ,    num_layers=1    ,    num_heads=1    ,    d_kv=4    ,    d_ff=2_048    ,    dropout_rate=0.1    ,    )

							lowerCAmelCase_      =     DDPMScheduler()

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




				def 					__a						( self    ,    _UpperCamelCase    ,    _UpperCamelCase=0					)    ->				int:
							if str(_snake_case					).startswith("mps"					):
										lowerCAmelCase_      =     torch.manual_seed(_snake_case					)
							else:
										lowerCAmelCase_      =     torch.Generator(device=_snake_case					).manual_seed(_snake_case					)
							lowerCAmelCase_      =     {
							    'input_tokens': [
							        [1_134, 90, 1_135, 1_133, 1_080, 112, 1_132, 1_080, 1_133, 1_079, 133, 1_132, 1_079, 1_133, 1] + [0] * 2_033
							    ],
							    'generator': generator,
							    'num_inference_steps': 4,
							    'output_type': 'mel',
							}
							return inputs




				def 					__a						( self					)    ->				Tuple:
							lowerCAmelCase_      =     'cpu'  # ensure determinism for the device-dependent torch.Generator
							lowerCAmelCase_      =     self.get_dummy_components()
							lowerCAmelCase_      =     SpectrogramDiffusionPipeline(**_snake_case					)
							lowerCAmelCase_      =     pipe.to(_snake_case					)
							pipe.set_progress_bar_config(disable=_snake_case					)

							lowerCAmelCase_      =     self.get_dummy_inputs(_snake_case					)
							lowerCAmelCase_      =     pipe(**_snake_case					)
							lowerCAmelCase_      =     output.audios

							lowerCAmelCase_      =     mel[0, -3:, -3:]

							assert mel_slice.shape == (3, 3)
							lowerCAmelCase_      =     np.array(
							    [-11.512_925, -4.788215, -0.46172905, -2.051715, -10.539_147, -10.970_963, -9.091634, 4.0, 4.0]					)
							assert np.abs(mel_slice.flatten() - expected_slice					).max() < 1e-2




				@skip_mps
				def 					__a						( self					)    ->				Tuple:
							return super().test_save_load_local()




				@skip_mps
				def 					__a						( self					)    ->				Optional[int]:
							return super().test_dict_tuple_outputs_equivalent()




				@skip_mps
				def 					__a						( self					)    ->				Union[str, Any]:
							return super().test_save_load_optional_components()




				@skip_mps
				def 					__a						( self					)    ->				int:
							return super().test_attention_slicing_forward_pass()




				def 					__a						( self					)    ->				Optional[Any]:
							pass




				def 					__a						( self					)    ->				Union[str, Any]:
							pass




				@skip_mps
				def 					__a						( self					)    ->				Optional[int]:
							return super().test_progress_bar()





@slow
@require_torch_gpu
@require_onnxruntime
@require_note_seq
class      _lowerCAmelCase (     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					)    ->				Dict:
							# TODO - test that pipeline can decode tokens in a callback
							# so that music can be played live
							lowerCAmelCase_      =     torch_device

							lowerCAmelCase_      =     SpectrogramDiffusionPipeline.from_pretrained("google/music-spectrogram-diffusion"					)
							lowerCAmelCase_      =     pipe.melgan
							lowerCAmelCase_      =     None

							lowerCAmelCase_      =     pipe.to(_snake_case					)
							pipe.set_progress_bar_config(disable=_snake_case					)

							def callback(_UpperCamelCase    ,    _UpperCamelCase					):
										# decode mel to audio
										lowerCAmelCase_      =     melgan(input_features=mel_output.astype(np.floataa					)					)[0]
										assert len(audio[0]					) == 81_920 * (step + 1)
										# simulate that audio is played
										return audio

							lowerCAmelCase_      =     MidiProcessor()
							lowerCAmelCase_      =     processor(_snake_case					)

							lowerCAmelCase_      =     input_tokens[:3]
							lowerCAmelCase_      =     torch.manual_seed(0					)
							pipe(_snake_case    ,    num_inference_steps=5    ,    generator=_snake_case    ,    callback=_snake_case    ,    output_type="mel"					)




				def 					__a						( self					)    ->				Dict:
							lowerCAmelCase_      =     torch_device

							lowerCAmelCase_      =     SpectrogramDiffusionPipeline.from_pretrained("google/music-spectrogram-diffusion"					)
							lowerCAmelCase_      =     pipe.to(_snake_case					)
							pipe.set_progress_bar_config(disable=_snake_case					)
							lowerCAmelCase_      =     MidiProcessor()

							lowerCAmelCase_      =     processor(_snake_case					)
							# just run two denoising loops
							lowerCAmelCase_      =     input_tokens[:2]

							lowerCAmelCase_      =     torch.manual_seed(0					)
							lowerCAmelCase_      =     pipe(_snake_case    ,    num_inference_steps=2    ,    generator=_snake_case					)

							lowerCAmelCase_      =     output.audios[0]

							assert abs(np.abs(_snake_case					).sum() - 3_612.841					) < 1e-1




				def 					__a						( self					)    ->				int:
							lowerCAmelCase_      =     torch_device

							lowerCAmelCase_      =     SpectrogramDiffusionPipeline.from_pretrained("google/music-spectrogram-diffusion"					)
							lowerCAmelCase_      =     pipe.to(_snake_case					)
							pipe.set_progress_bar_config(disable=_snake_case					)

							lowerCAmelCase_      =     MidiProcessor()

							lowerCAmelCase_      =     processor(_snake_case					)

							# just run 4 denoising loops
							lowerCAmelCase_      =     input_tokens[:4]

							lowerCAmelCase_      =     torch.manual_seed(0					)
							lowerCAmelCase_      =     pipe(_snake_case    ,    num_inference_steps=100    ,    generator=_snake_case					)

							lowerCAmelCase_      =     output.audios[0]
							assert abs(np.abs(_snake_case					).sum() - 9_389.1_111					) < 5e-2





import math





class      _lowerCAmelCase :




				def __init__( self    ,    _UpperCamelCase					)    ->				Dict:
							lowerCAmelCase_      =     len(_UpperCamelCase					)
							lowerCAmelCase_      =     [0] * (
							    4 * self.N
							)  # approximate the overall size of segment tree with array N
							self.build(1    ,    0    ,    self.N - 1					)




				def 					__a						( self    ,    _UpperCamelCase					)    ->				List[str]:
							return idx * 2




				def 					__a						( self    ,    _UpperCamelCase					)    ->				Tuple:
							return idx * 2 + 1




				def 					__a						( self    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase					)    ->				int:  # noqa: E741
							if l == r:
										lowerCAmelCase_      =     A[l]
							else:
										lowerCAmelCase_      =     (l + r) // 2
										self.build(self.left(_UpperCamelCase					)    ,    _UpperCamelCase    ,    _UpperCamelCase					)
										self.build(self.right(_UpperCamelCase					)    ,    mid + 1    ,    _UpperCamelCase					)
										lowerCAmelCase_      =     max(self.st[self.left(_UpperCamelCase					)]    ,    self.st[self.right(_UpperCamelCase					)]					)




				def 					__a						( self    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase					)    ->				int:
							return self.update_recursive(1    ,    0    ,    self.N - 1    ,    a - 1    ,    b - 1    ,    _UpperCamelCase					)




				def 					__a						( self    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase					)    ->				str:  # noqa: E741
							if r < a or l > b:
										return True
							if l == r:
										lowerCAmelCase_      =     val
										return True
							lowerCAmelCase_      =     (l + r) // 2
							self.update_recursive(self.left(_UpperCamelCase					)    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase					)
							self.update_recursive(self.right(_UpperCamelCase					)    ,    mid + 1    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase					)
							lowerCAmelCase_      =     max(self.st[self.left(_UpperCamelCase					)]    ,    self.st[self.right(_UpperCamelCase					)]					)
							return True




				def 					__a						( self    ,    _UpperCamelCase    ,    _UpperCamelCase					)    ->				Optional[Any]:
							return self.query_recursive(1    ,    0    ,    self.N - 1    ,    a - 1    ,    b - 1					)




				def 					__a						( self    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase					)    ->				Any:  # noqa: E741
							if r < a or l > b:
										return -math.inf
							if l >= a and r <= b:
										return self.st[idx]
							lowerCAmelCase_      =     (l + r) // 2
							lowerCAmelCase_      =     self.query_recursive(self.left(_UpperCamelCase					)    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase					)
							lowerCAmelCase_      =     self.query_recursive(self.right(_UpperCamelCase					)    ,    mid + 1    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase					)
							return max(_UpperCamelCase    ,    _UpperCamelCase					)




				def 					__a						( self					)    ->				int:
							lowerCAmelCase_      =     []
							for i in range(1    ,    N + 1					):
										show_list += [self.query(_UpperCamelCase    ,    _UpperCamelCase					)]
							print(_UpperCamelCase					)


if __name__ == "__main__":
			_A       =					[1, 2, -4, 7, 3, -5, 6, 11, -20, 9, 14, 15, 5, 2, -8]
			_A       =					15
			_A       =					SegmentTree(A)
			print(segt.query(4, 6))
			print(segt.query(7, 11))
			print(segt.query(7, 12))
			segt.update(1, 3, 1_11)
			print(segt.query(1, 15))
			segt.update(7, 8, 2_35)
			segt.show_data()



