






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   SCREAMING_SNAKE_CASE (       snake_case     ,		unittest.TestCase    ):





							"""simple docstring"""

							A_       =    SpectrogramDiffusionPipeline
							A_       =    PipelineTesterMixin.required_optional_params - {
							    "callback",
							    "latents",
							    "callback_steps",
							    "output_type",
							    "num_images_per_prompt",
							}
							A_       =    False
							A_       =    False
							A_       =    TOKENS_TO_AUDIO_GENERATION_PARAMS
							A_       =    TOKENS_TO_AUDIO_GENERATION_BATCH_PARAMS

							def 					__A       (     self:      int      )			-> Optional[int]:
												torch.manual_seed(0      )
												_A									=       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'''						,   )

												_A									=       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'''						,   )

												_A									=       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						,   )

												_A									=       DDPMScheduler()

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

							def 					__A       (     self:      str						,   __A:      Optional[Any]						,   __A:      Optional[int]=0      )			-> str:
												if str(__A      ).startswith('''mps'''      ):
																	_A									=       torch.manual_seed(__A      )
												else:
																	_A									=       torch.Generator(device=__A      ).manual_seed(__A      )
												_A									=       {
												    '''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 					__A       (     self:      int      )			-> str:
												_A									=       '''cpu'''  # ensure determinism for the device-dependent torch.Generator
												_A									=       self.get_dummy_components()
												_A									=       SpectrogramDiffusionPipeline(**__A      )
												_A									=       pipe.to(__A      )
												pipe.set_progress_bar_config(disable=__A      )

												_A									=       self.get_dummy_inputs(__A      )
												_A									=       pipe(**__A      )
												_A									=       output.audios

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

												assert mel_slice.shape == (3, 3)
												_A									=       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 					__A       (     self:      Tuple      )			-> Optional[int]:
												return super().test_save_load_local()

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

							@skip_mps
							def 					__A       (     self:      Dict      )			-> Tuple:
												return super().test_save_load_optional_components()

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

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

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



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





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





							"""simple docstring"""

							def 					__A       (     self:      int      )			-> Dict:
												# clean up the VRAM after each test
												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()

							def 					__A       (     self:      Union[str, Any]      )			-> List[str]:
												# TODO - test that pipeline can decode tokens in a callback
												# so that music can be played live
												_A									=       torch_device

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

												_A									=       pipe.to(__A      )
												pipe.set_progress_bar_config(disable=__A      )

												def callback(__A:      Any						,   __A:      Optional[int]      ):
																	# decode mel to audio
																	_A									=       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

												_A									=       MidiProcessor()
												_A									=       processor(__A      )

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

							def 					__A       (     self:      Any      )			-> str:
												_A									=       torch_device

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

												_A									=       processor(__A      )
												# just run two denoising loops
												_A									=       input_tokens[:2]

												_A									=       torch.manual_seed(0      )
												_A									=       pipe(__A						,   num_inference_steps=2						,   generator=__A      )

												_A									=       output.audios[0]

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



							def 					__A       (     self:      Any      )			-> str:
												_A									=       torch_device

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

												_A									=       MidiProcessor()

												_A									=       processor(__A      )

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

												_A									=       torch.manual_seed(0      )
												_A									=       pipe(__A						,   num_inference_steps=1_00						,   generator=__A      )

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











from __future__ import annotations

from collections.abc import Generator




def    __A    (   _lowercase       ):

					'''simple docstring'''







					if not isinstance(_lowercase    ,	_lowercase       ) or n < 1:
										raise Exception('''Sequence only defined for positive integers'''       )

					yield n
					while n != 1:
										if n % 2 == 0:
															n //= 2
										else:
															_A									=       3 * n + 1
										yield n




def    __A    (   ):

					'''simple docstring'''







					_A									=       43
					_A									=       tuple(collatz_sequence(_lowercase       )       )
					print(_lowercase       )
					print(f"""Collatz sequence from {n} took {len(_lowercase       )} steps."""       )


if __name__ == "__main__":
				main()



