



'''simple docstring'''





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	:   int    	="./tests/fixtures/elise_format0.mid"

@unittest.skip("The note-seq package currently throws an error on import"	)
class      snake_case__       (A__      ,			unittest.TestCase	):







						"""simple docstring"""




						__lowerCAmelCase  :Optional[int]			      =  SpectrogramDiffusionPipeline
						__lowerCAmelCase  :Optional[Any]			      =  PipelineTesterMixin.required_optional_params - {
						    "callback",
						    "latents",
						    "callback_steps",
						    "output_type",
						    "num_images_per_prompt",
						}
						__lowerCAmelCase  :Dict			      =  False
						__lowerCAmelCase  :Any			      =  False
						__lowerCAmelCase  :str			      =  TOKENS_TO_AUDIO_GENERATION_PARAMS
						__lowerCAmelCase  :str			      =  TOKENS_TO_AUDIO_GENERATION_BATCH_PARAMS

						def 							SCREAMING_SNAKE_CASE__(     self						)      ->   List[Any]:
									"""simple docstring"""




									torch.manual_seed(0						)
									a__     :		Optional[int]		 =						SpectrogramNotesEncoder(
									    max_length=2_0_4_8      ,     vocab_size=1_5_3_6      ,     d_model=7_6_8      ,     dropout_rate=0.1      ,     num_layers=1      ,     num_heads=1      ,     d_kv=4      ,     d_ff=2_0_4_8      ,     feed_forward_proj="""gated-gelu"""      ,     )

									a__     :		Dict		 =						SpectrogramContEncoder(
									    input_dims=1_2_8      ,     targets_context_length=2_5_6      ,     d_model=7_6_8      ,     dropout_rate=0.1      ,     num_layers=1      ,     num_heads=1      ,     d_kv=4      ,     d_ff=2_0_4_8      ,     feed_forward_proj="""gated-gelu"""      ,     )

									a__     :		str		 =						TaFilmDecoder(
									    input_dims=1_2_8      ,     targets_length=2_5_6      ,     max_decoder_noise_time=2_0_0_0_0.0      ,     d_model=7_6_8      ,     num_layers=1      ,     num_heads=1      ,     d_kv=4      ,     d_ff=2_0_4_8      ,     dropout_rate=0.1      ,     )

									a__     :		Dict		 =						DDPMScheduler()

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

						def 							SCREAMING_SNAKE_CASE__(     self      ,     __lowercase      ,     __lowercase=0						)      ->   Dict:
									"""simple docstring"""




									if str(__lowercase						).startswith("""mps"""						):
												a__     :		List[Any]		 =						torch.manual_seed(__lowercase						)
									else:
												a__     :		Any		 =						torch.Generator(device=__lowercase						).manual_seed(__lowercase						)
									a__     :		Any		 =						{
									    """input_tokens""": [
									        [1_1_3_4, 9_0, 1_1_3_5, 1_1_3_3, 1_0_8_0, 1_1_2, 1_1_3_2, 1_0_8_0, 1_1_3_3, 1_0_7_9, 1_3_3, 1_1_3_2, 1_0_7_9, 1_1_3_3, 1] + [0] * 2_0_3_3
									    ],
									    """generator""": generator,
									    """num_inference_steps""": 4,
									    """output_type""": """mel""",
									}
									return inputs

						def 							SCREAMING_SNAKE_CASE__(     self						)      ->   List[str]:
									"""simple docstring"""




									a__     :		List[str]		 =						"""cpu"""  # ensure determinism for the device-dependent torch.Generator
									a__     :		List[Any]		 =						self.get_dummy_components()
									a__     :		Any		 =						SpectrogramDiffusionPipeline(**__lowercase						)
									a__     :		List[Any]		 =						pipe.to(__lowercase						)
									pipe.set_progress_bar_config(disable=__lowercase						)

									a__     :		List[str]		 =						self.get_dummy_inputs(__lowercase						)
									a__     :		str		 =						pipe(**__lowercase						)
									a__     :		Optional[int]		 =						output.audios

									a__     :		Optional[int]		 =						mel[0, -3:, -3:]

									assert mel_slice.shape == (3, 3)
									a__     :		List[Any]		 =						np.array(
									    [-1_1.5_1_2_9_2_5, -4.7_8_8_2_1_5, -0.4_6_1_7_2_9_0_5, -2.0_5_1_7_1_5, -1_0.5_3_9_1_4_7, -1_0.9_7_0_9_6_3, -9.0_9_1_6_3_4, 4.0, 4.0]						)
									assert np.abs(mel_slice.flatten() - expected_slice						).max() < 1E-2

						@skip_mps
						def 							SCREAMING_SNAKE_CASE__(     self						)      ->   int:
									"""simple docstring"""




									return super().test_save_load_local()

						@skip_mps
						def 							SCREAMING_SNAKE_CASE__(     self						)      ->   Optional[Any]:
									"""simple docstring"""




									return super().test_dict_tuple_outputs_equivalent()

						@skip_mps
						def 							SCREAMING_SNAKE_CASE__(     self						)      ->   List[Any]:
									"""simple docstring"""




									return super().test_save_load_optional_components()

						@skip_mps
						def 							SCREAMING_SNAKE_CASE__(     self						)      ->   List[str]:
									"""simple docstring"""




									return super().test_attention_slicing_forward_pass()

						def 							SCREAMING_SNAKE_CASE__(     self						)      ->   Optional[Any]:
									"""simple docstring"""




									pass

						def 							SCREAMING_SNAKE_CASE__(     self						)      ->   Optional[int]:
									"""simple docstring"""




									pass

						@skip_mps
						def 							SCREAMING_SNAKE_CASE__(     self						)      ->   Any:
									"""simple docstring"""




									return super().test_progress_bar()




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







						"""simple docstring"""




						def 							SCREAMING_SNAKE_CASE__(     self						)      ->   int:
									"""simple docstring"""




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

						def 							SCREAMING_SNAKE_CASE__(     self						)      ->   List[str]:
									"""simple docstring"""




									a__     :		List[str]		 =						torch_device

									a__     :		List[Any]		 =						SpectrogramDiffusionPipeline.from_pretrained("""google/music-spectrogram-diffusion"""						)
									a__     :		Dict		 =						pipe.melgan
									a__     :		List[Any]		 =						None

									a__     :		int		 =						pipe.to(__lowercase						)
									pipe.set_progress_bar_config(disable=__lowercase						)

									def callback(__lowercase      ,     __lowercase						):
												# decode mel to audio
												a__     :		Union[str, Any]		 =						melgan(input_features=mel_output.astype(np.floataa						)						)[0]
												assert len(audio[0]						) == 8_1_9_2_0 * (step + 1)
												# simulate that audio is played
												return audio

									a__     :		Optional[int]		 =						MidiProcessor()
									a__     :		str		 =						processor(__lowercase						)

									a__     :		Any		 =						input_tokens[:3]
									a__     :		Optional[int]		 =						torch.manual_seed(0						)
									pipe(__lowercase      ,     num_inference_steps=5      ,     generator=__lowercase      ,     callback=__lowercase      ,     output_type="""mel"""						)

						def 							SCREAMING_SNAKE_CASE__(     self						)      ->   Optional[int]:
									"""simple docstring"""




									a__     :		List[str]		 =						torch_device

									a__     :		List[str]		 =						SpectrogramDiffusionPipeline.from_pretrained("""google/music-spectrogram-diffusion"""						)
									a__     :		str		 =						pipe.to(__lowercase						)
									pipe.set_progress_bar_config(disable=__lowercase						)
									a__     :		Optional[int]		 =						MidiProcessor()

									a__     :		str		 =						processor(__lowercase						)
									# just run two denoising loops
									a__     :		List[Any]		 =						input_tokens[:2]

									a__     :		int		 =						torch.manual_seed(0						)
									a__     :		Dict		 =						pipe(__lowercase      ,     num_inference_steps=2      ,     generator=__lowercase						)

									a__     :		int		 =						output.audios[0]

									assert abs(np.abs(__lowercase						).sum() - 3_6_1_2.8_4_1						) < 1E-1

						def 							SCREAMING_SNAKE_CASE__(     self						)      ->   Tuple:
									"""simple docstring"""




									a__     :		str		 =						torch_device

									a__     :		str		 =						SpectrogramDiffusionPipeline.from_pretrained("""google/music-spectrogram-diffusion"""						)
									a__     :		int		 =						pipe.to(__lowercase						)
									pipe.set_progress_bar_config(disable=__lowercase						)

									a__     :		Union[str, Any]		 =						MidiProcessor()

									a__     :		Union[str, Any]		 =						processor(__lowercase						)

									# just run 4 denoising loops
									a__     :		Optional[int]		 =						input_tokens[:4]

									a__     :		Optional[Any]		 =						torch.manual_seed(0						)
									a__     :		List[str]		 =						pipe(__lowercase      ,     num_inference_steps=1_0_0      ,     generator=__lowercase						)

									a__     :		List[str]		 =						output.audios[0]
									assert abs(np.abs(__lowercase						).sum() - 9_3_8_9.1_1_1_1						) < 5E-2









from __future__ import annotations

from abc import abstractmethod
from collections.abc import Iterable
from typing import Generic, Protocol, TypeVar

class      snake_case__       (A__	):







						"""simple docstring"""




						@abstractmethod
						def __lt__(     self      ,     __lowercase						)      ->   bool:
									"""simple docstring"""




									pass

						@abstractmethod
						def __gt__(     self      ,     __lowercase						)      ->   bool:
									"""simple docstring"""




									pass

						@abstractmethod
						def __eq__(     self      ,     __lowercase						)      ->   bool:
									"""simple docstring"""




									pass




_lowercase	:   List[str]    	=TypeVar("T", bound=Comparable)

class      snake_case__       (Generic[T]	):







						"""simple docstring"""




						def __init__(     self						)      ->   None:
									"""simple docstring"""




									a__     :		list[T]		 =						[]
									a__     :		int		 =						0

						def __repr__(     self						)      ->   str:
									"""simple docstring"""




									return str(self.h						)

						def 							SCREAMING_SNAKE_CASE__(     self      ,     __lowercase						)      ->   int | None:
									"""simple docstring"""




									if child_idx > 0:
												return (child_idx - 1) // 2
									return None

						def 							SCREAMING_SNAKE_CASE__(     self      ,     __lowercase						)      ->   int | None:
									"""simple docstring"""




									a__     :		int		 =						2 * parent_idx + 1
									if left_child_index < self.heap_size:
												return left_child_index
									return None

						def 							SCREAMING_SNAKE_CASE__(     self      ,     __lowercase						)      ->   int | None:
									"""simple docstring"""




									a__     :		Optional[Any]		 =						2 * parent_idx + 2
									if right_child_index < self.heap_size:
												return right_child_index
									return None

						def 							SCREAMING_SNAKE_CASE__(     self      ,     __lowercase						)      ->   None:
									"""simple docstring"""




									if index < self.heap_size:
												a__     :		int		 =						index
												a__     :		Union[str, Any]		 =						self.left_child_idx(__lowercase						)
												a__     :		Any		 =						self.right_child_idx(__lowercase						)
												# check which child is larger than its parent
												if left_child is not None and self.h[left_child] > self.h[violation]:
															a__     :		Union[str, Any]		 =						left_child
												if right_child is not None and self.h[right_child] > self.h[violation]:
															a__     :		List[Any]		 =						right_child
												# if violation indeed exists
												if violation != index:
															# swap to fix the violation
															a__ ,		a__     :		Tuple		 =						self.h[index], self.h[violation]
															# fix the subsequent violation recursively if any
															self.max_heapify(__lowercase						)

						def 							SCREAMING_SNAKE_CASE__(     self      ,     __lowercase						)      ->   None:
									"""simple docstring"""




									a__     :		Tuple		 =						list(__lowercase						)
									a__     :		Union[str, Any]		 =						len(self.h						)
									if self.heap_size > 1:
												# max_heapify from right to left but exclude leaves (last level)
												for i in range(self.heap_size // 2 - 1      ,     -1      ,     -1						):
															self.max_heapify(__lowercase						)

						def 							SCREAMING_SNAKE_CASE__(     self						)      ->   T:
									"""simple docstring"""




									if self.heap_size >= 2:
												a__     :		Tuple		 =						self.h[0]
												a__     :		Union[str, Any]		 =						self.h.pop(-1						)
												self.heap_size -= 1
												self.max_heapify(0						)
												return me
									elif self.heap_size == 1:
												self.heap_size -= 1
												return self.h.pop(-1						)
									else:
												raise Exception("""Empty heap"""						)

						def 							SCREAMING_SNAKE_CASE__(     self      ,     __lowercase						)      ->   None:
									"""simple docstring"""




									self.h.append(__lowercase						)
									a__     :		Optional[int]		 =						(self.heap_size - 1) // 2
									self.heap_size += 1
									while idx >= 0:
												self.max_heapify(__lowercase						)
												a__     :		Union[str, Any]		 =						(idx - 1) // 2

						def 							SCREAMING_SNAKE_CASE__(     self						)      ->   None:
									"""simple docstring"""




									a__     :		Any		 =						self.heap_size
									for j in range(size - 1      ,     0      ,     -1						):
												a__ ,		a__     :		Dict		 =						self.h[j], self.h[0]
												self.heap_size -= 1
												self.max_heapify(0						)
									a__     :		List[Any]		 =						size


if __name__ == "__main__":
		import doctest

		# run doc test
		doctest.testmod()

		# demo
		for unsorted in [
		    [0],
		    [2],
		    [3, 5],
		    [5, 3],
		    [5, 5],
		    [0, 0, 0, 0],
		    [1, 1, 1, 1],
		    [2, 2, 3, 5],
		    [0, 2, 2, 3, 5],
		    [2, 5, 3, 0, 2, 3, 0, 3],
		    [6, 1, 2, 7, 9, 3, 4, 5, 10, 8],
		    [103, 9, 1, 7, 11, 15, 25, 201, 209, 107, 5],
		    [-45, -2, -5],
		]:
				print(f'unsorted array: {unsorted}')

				_lowercase	:   Heap[int]    	=Heap()
				heap.build_max_heap(unsorted)
				print(f'after build heap: {heap}')

				print(f'max value: {heap.extract_max()}')
				print(f'after max value removed: {heap}')

				heap.insert(100)
				print(f'after new value 100 inserted: {heap}')

				heap.heap_sort()
				print(f'heap-sorted array: {heap}\n')




