

"""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()


a__			:						Any							=			"""./tests/fixtures/elise_format0.mid"""


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







						def 							_UpperCamelCase     (self				)      ->    int:
											torch.manual_seed(0				)
											__lowercase        =      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'''  ,				)

											__lowercase        =      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'''  ,				)

											__lowercase        =      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  ,				)

											__lowercase        =      DDPMScheduler()

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







						def 							_UpperCamelCase     (self  ,				UpperCAmelCase_  ,				UpperCAmelCase_=0				)      ->    int:
											if str(lowercase_				).startswith('''mps'''				):
																__lowercase        =      torch.manual_seed(lowercase_				)
											else:
																__lowercase        =      torch.Generator(device=lowercase_				).manual_seed(lowercase_				)
											__lowercase        =      {
											    "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 							_UpperCamelCase     (self				)      ->    int:
											__lowercase        =      "cpu"  # ensure determinism for the device-dependent torch.Generator
											__lowercase        =      self.get_dummy_components()
											__lowercase        =      SpectrogramDiffusionPipeline(**lowercase_				)
											__lowercase        =      pipe.to(lowercase_				)
											pipe.set_progress_bar_config(disable=lowercase_				)

											__lowercase        =      self.get_dummy_inputs(lowercase_				)
											__lowercase        =      pipe(**lowercase_				)
											__lowercase        =      output.audios

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

											assert mel_slice.shape == (3, 3)
											__lowercase        =      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 							_UpperCamelCase     (self				)      ->    Tuple:
											return super().test_save_load_local()







						@skip_mps
						def 							_UpperCamelCase     (self				)      ->    Tuple:
											return super().test_dict_tuple_outputs_equivalent()







						@skip_mps
						def 							_UpperCamelCase     (self				)      ->    List[Any]:
											return super().test_save_load_optional_components()







						@skip_mps
						def 							_UpperCamelCase     (self				)      ->    List[Any]:
											return super().test_attention_slicing_forward_pass()







						def 							_UpperCamelCase     (self				)      ->    Optional[int]:
											pass







						def 							_UpperCamelCase     (self				)      ->    Tuple:
											pass







						@skip_mps
						def 							_UpperCamelCase     (self				)      ->    List[Any]:
											return super().test_progress_bar()



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







						def 							_UpperCamelCase     (self				)      ->    Union[str, Any]:
											# clean up the VRAM after each test
											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()







						def 							_UpperCamelCase     (self				)      ->    Optional[int]:
											# TODO - test that pipeline can decode tokens in a callback
											# so that music can be played live
											__lowercase        =      torch_device

											__lowercase        =      SpectrogramDiffusionPipeline.from_pretrained('''google/music-spectrogram-diffusion'''				)
											__lowercase        =      pipe.melgan
											__lowercase        =      None

											__lowercase        =      pipe.to(lowercase_				)
											pipe.set_progress_bar_config(disable=lowercase_				)

											def callback(UpperCAmelCase_  ,				UpperCAmelCase_				):
																# decode mel to audio
																__lowercase        =      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

											__lowercase        =      MidiProcessor()
											__lowercase        =      processor(lowercase_				)

											__lowercase        =      input_tokens[:3]
											__lowercase        =      torch.manual_seed(0				)
											pipe(lowercase_  ,				num_inference_steps=5  ,				generator=lowercase_  ,				callback=lowercase_  ,				output_type='''mel'''				)







						def 							_UpperCamelCase     (self				)      ->    Tuple:
											__lowercase        =      torch_device

											__lowercase        =      SpectrogramDiffusionPipeline.from_pretrained('''google/music-spectrogram-diffusion'''				)
											__lowercase        =      pipe.to(lowercase_				)
											pipe.set_progress_bar_config(disable=lowercase_				)
											__lowercase        =      MidiProcessor()

											__lowercase        =      processor(lowercase_				)
											# just run two denoising loops
											__lowercase        =      input_tokens[:2]

											__lowercase        =      torch.manual_seed(0				)
											__lowercase        =      pipe(lowercase_  ,				num_inference_steps=2  ,				generator=lowercase_				)

											__lowercase        =      output.audios[0]

											assert abs(np.abs(lowercase_				).sum() - 3_6_1_2.8_4_1				) < 1e-1







						def 							_UpperCamelCase     (self				)      ->    Optional[Any]:
											__lowercase        =      torch_device

											__lowercase        =      SpectrogramDiffusionPipeline.from_pretrained('''google/music-spectrogram-diffusion'''				)
											__lowercase        =      pipe.to(lowercase_				)
											pipe.set_progress_bar_config(disable=lowercase_				)

											__lowercase        =      MidiProcessor()

											__lowercase        =      processor(lowercase_				)

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

											__lowercase        =      torch.manual_seed(0				)
											__lowercase        =      pipe(lowercase_  ,				num_inference_steps=1_0_0  ,				generator=lowercase_				)

											__lowercase        =      output.audios[0]
											assert abs(np.abs(lowercase_				).sum() - 9_3_8_9.1_1_1_1				) < 5e-2




'''simple docstring'''
def     UpperCamelCase_  (						__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							):
    """simple docstring"""






    if right <= left:
        return
    snake_case_			:     Dict        =    left
    snake_case_			:     Tuple        =    right
    snake_case_			:     List[str]        =    sorting[left]
    while i <= b:
        if sorting[i] < pivot:
            snake_case_      ,			snake_case_			:     Union[str, Any]        =    sorting[i], sorting[a]
            a += 1
            i += 1
        elif sorting[i] > pivot:
            snake_case_      ,			snake_case_			:     List[str]        =    sorting[i], sorting[b]
            b -= 1
        else:
            i += 1
    quick_sort_apartition(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		a - 1							)
    quick_sort_apartition(__SCREAMING_SNAKE_CASE,		b + 1,		__SCREAMING_SNAKE_CASE							)

def     UpperCamelCase_  (						__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							):
    """simple docstring"""






    if left < right:
        snake_case_			:     Dict        =    lomuto_partition(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							)
        quick_sort_lomuto_partition(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		pivot_index - 1							)
        quick_sort_lomuto_partition(__SCREAMING_SNAKE_CASE,		pivot_index + 1,		__SCREAMING_SNAKE_CASE							)

def     UpperCamelCase_  (						__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							):
    """simple docstring"""






    snake_case_			:     List[Any]        =    sorting[right]
    snake_case_			:     Optional[int]        =    left
    for i in range(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							):
        if sorting[i] < pivot:
            snake_case_      ,			snake_case_			:     Dict        =    sorting[i], sorting[store_index]
            store_index += 1
    snake_case_      ,			snake_case_			:     Optional[Any]        =    sorting[store_index], sorting[right]
    return store_index

def     UpperCamelCase_  (						__SCREAMING_SNAKE_CASE							):
    """simple docstring"""






    if len(__SCREAMING_SNAKE_CASE							) <= 1:
        return sorting
    return (
        three_way_radix_quicksort([i for i in sorting if i < sorting[0]]							)
        + [i for i in sorting if i == sorting[0]]
        + three_way_radix_quicksort([i for i in sorting if i > sorting[0]]							)
    )


if __name__ == "__main__":
  import doctest

  doctest.testmod(verbose=True)

  a_           =    input("Enter numbers separated by a comma:\n").strip()
  a_           =    [int(item) for item in user_input.split(",")]
  quick_sort_apartition(unsorted, 0, len(unsorted) - 1)
  print(unsorted)




