
'''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_					(						snake_case__     ,    unittest.TestCase       ):
 UpperCAmelCase_		     =    SpectrogramDiffusionPipeline
 UpperCAmelCase_		     =    PipelineTesterMixin.required_optional_params - {
     """callback""",
     """latents""",
     """callback_steps""",
     """output_type""",
     """num_images_per_prompt""",
 }
 UpperCAmelCase_		     =    False
 UpperCAmelCase_		     =    False
 UpperCAmelCase_		     =    TOKENS_TO_AUDIO_GENERATION_PARAMS
 UpperCAmelCase_		     =    TOKENS_TO_AUDIO_GENERATION_BATCH_PARAMS





 def 							snake_case__		(     self):
     torch.manual_seed(0)
     snake_case_			:     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"			,							)

     snake_case_			:     Optional[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"			,							)

     snake_case_			:     str        =    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			,							)

     snake_case_			:     List[Any]        =    DDPMScheduler()

     snake_case_			:     Optional[Any]        =    {
         "notes_encoder": notes_encoder.eval(),
         "continuous_encoder": continuous_encoder.eval(),
         "decoder": decoder.eval(),
         "scheduler": scheduler,
         "melgan": None,
     }
     return components





 def 							snake_case__		(     self			,							lowercase_			,							lowercase_=0):
     if str(lowercase_).startswith("mps"):
         snake_case_			:     Any        =    torch.manual_seed(lowercase_)
     else:
         snake_case_			:     Union[str, Any]        =    torch.Generator(device=lowercase_).manual_seed(lowercase_)
     snake_case_			:     Dict        =    {
         "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 							snake_case__		(     self):
     snake_case_			:     Tuple        =    "cpu"  # ensure determinism for the device-dependent torch.Generator
     snake_case_			:     Any        =    self.get_dummy_components()
     snake_case_			:     Tuple        =    SpectrogramDiffusionPipeline(**lowercase_)
     snake_case_			:     Optional[Any]        =    pipe.to(lowercase_)
     pipe.set_progress_bar_config(disable=lowercase_)

     snake_case_			:     List[Any]        =    self.get_dummy_inputs(lowercase_)
     snake_case_			:     int        =    pipe(**lowercase_)
     snake_case_			:     Optional[int]        =    output.audios

     snake_case_			:     Dict        =    mel[0, -3:, -3:]

     assert mel_slice.shape == (3, 3)
     snake_case_			:     Optional[int]        =    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 							snake_case__		(     self):
     return super().test_save_load_local()





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





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





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





 def 							snake_case__		(     self):
     pass





 def 							snake_case__		(     self):
     pass





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






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





 def 							snake_case__		(     self):
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()





 def 							snake_case__		(     self):
     # TODO - test that pipeline can decode tokens in a callback
     # so that music can be played live
     snake_case_			:     str        =    torch_device

     snake_case_			:     List[str]        =    SpectrogramDiffusionPipeline.from_pretrained("google/music-spectrogram-diffusion")
     snake_case_			:     List[str]        =    pipe.melgan
     snake_case_			:     Optional[int]        =    None

     snake_case_			:     Union[str, Any]        =    pipe.to(lowercase_)
     pipe.set_progress_bar_config(disable=lowercase_)

     def callback(lowercase_			,							lowercase_):
         # decode mel to audio
         snake_case_			:     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

     snake_case_			:     Any        =    MidiProcessor()
     snake_case_			:     List[Any]        =    processor(lowercase_)

     snake_case_			:     Union[str, Any]        =    input_tokens[:3]
     snake_case_			:     int        =    torch.manual_seed(0)
     pipe(lowercase_			,							num_inference_steps=5			,							generator=lowercase_			,							callback=lowercase_			,							output_type="mel")





 def 							snake_case__		(     self):
     snake_case_			:     int        =    torch_device

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

     snake_case_			:     int        =    processor(lowercase_)
     # just run two denoising loops
     snake_case_			:     Union[str, Any]        =    input_tokens[:2]

     snake_case_			:     Union[str, Any]        =    torch.manual_seed(0)
     snake_case_			:     Union[str, Any]        =    pipe(lowercase_			,							num_inference_steps=2			,							generator=lowercase_)

     snake_case_			:     int        =    output.audios[0]

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





 def 							snake_case__		(     self):
     snake_case_			:     str        =    torch_device

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

     snake_case_			:     Dict        =    MidiProcessor()

     snake_case_			:     Union[str, Any]        =    processor(lowercase_)

     # just run 4 denoising loops
     snake_case_			:     List[Any]        =    input_tokens[:4]

     snake_case_			:     List[str]        =    torch.manual_seed(0)
     snake_case_			:     int        =    pipe(lowercase_			,							num_inference_steps=1_00			,							generator=lowercase_)

     snake_case_			:     List[Any]        =    output.audios[0]
     assert abs(np.abs(lowercase_).sum() - 9_389.1_111) < 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)




