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






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

				def 		_a	(      self      ):
										torch.manual_seed(0      )
										lowerCamelCase__					=SpectrogramNotesEncoder(
										    max_length=2048	, vocab_size=1536	, d_model=768	, dropout_rate=0.1	, num_layers=1	, num_heads=1	, d_kv=4	, d_ff=2048	, 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=2048	, feed_forward_proj="gated-gelu"	, )

										lowerCamelCase__					=TaFilmDecoder(
										    input_dims=128	, targets_length=256	, max_decoder_noise_time=20000.0	, d_model=768	, num_layers=1	, num_heads=1	, d_kv=4	, d_ff=2048	, 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	, _lowerCamelCase	, _lowerCamelCase=0      ):
										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": [
										        [1134, 90, 1135, 1133, 1080, 112, 1132, 1080, 1133, 1079, 133, 1132, 1079, 1133, 1] + [0] * 2033
										    ],
										    "generator": generator,
										    "num_inference_steps": 4,
										    "output_type": "mel",
										}
										return inputs

				def 		_a	(      self      ):
										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(
										    [-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 		_a	(      self      ):
										return super().test_save_load_local()

				@skip_mps
				def 		_a	(      self      ):
										return super().test_dict_tuple_outputs_equivalent()

				@skip_mps
				def 		_a	(      self      ):
										return super().test_save_load_optional_components()

				@skip_mps
				def 		_a	(      self      ):
										return super().test_attention_slicing_forward_pass()

				def 		_a	(      self      ):
										pass

				def 		_a	(      self      ):
										pass






				@skip_mps
				def 		_a	(      self      ):
										return super().test_progress_bar()






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

				def 		_a	(      self      ):
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()

				def 		_a	(      self      ):
										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(_lowerCamelCase	, _lowerCamelCase      ):
																# decode mel to audio
																lowerCamelCase__					=melgan(input_features=mel_output.astype(np.floataa      )      )[0]
																assert len(audio[0]      ) == 81920 * (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      ):
										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_6_1_2.8_4_1      ) < 1E-1






				def 		_a	(      self      ):
										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_3_8_9.1_1_1_1      ) < 5E-2










"""simple docstring"""



def      SCREAMING_SNAKE_CASE__    (   snake_case    :      list       , snake_case    :      int       , snake_case    :      int							)->						None:






     '''simple docstring'''



     if right <= left:
          return
     UpperCAmelCase__		:  str				=				left
     UpperCAmelCase__		:  List[str]				=				right
     UpperCAmelCase__		:  List[Any]				=				sorting[left]
     while i <= b:
          if sorting[i] < pivot:
               UpperCAmelCase__				,							UpperCAmelCase__		:  List[str]				=				sorting[i], sorting[a]
               a += 1
               i += 1
          elif sorting[i] > pivot:
               UpperCAmelCase__				,							UpperCAmelCase__		:  Optional[int]				=				sorting[i], sorting[b]
               b -= 1
          else:
               i += 1
     quick_sort_apartition(snake_case       , snake_case       , a - 1							)
     quick_sort_apartition(snake_case       , b + 1       , snake_case							)


def      SCREAMING_SNAKE_CASE__    (   snake_case    :      list       , snake_case    :      int       , snake_case    :      int							)->						None:






     '''simple docstring'''



     if left < right:
          UpperCAmelCase__		:  int				=				lomuto_partition(snake_case       , snake_case       , snake_case							)
          quick_sort_lomuto_partition(snake_case       , snake_case       , pivot_index - 1							)
          quick_sort_lomuto_partition(snake_case       , pivot_index + 1       , snake_case							)


def      SCREAMING_SNAKE_CASE__    (   snake_case    :      list       , snake_case    :      int       , snake_case    :      int							)->						int:






     '''simple docstring'''



     UpperCAmelCase__		:  Union[str, Any]				=				sorting[right]
     UpperCAmelCase__		:  Optional[Any]				=				left
     for i in range(snake_case       , snake_case							):
          if sorting[i] < pivot:
               UpperCAmelCase__				,							UpperCAmelCase__		:  List[Any]				=				sorting[i], sorting[store_index]
               store_index += 1
     UpperCAmelCase__				,							UpperCAmelCase__		:  List[Any]				=				sorting[store_index], sorting[right]
     return store_index


def      SCREAMING_SNAKE_CASE__    (   snake_case    :      list							)->						list:






     '''simple docstring'''



     if len(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)

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


