"""simple docstring"""


from __future__ import annotations


def   __UpperCAmelCase     (	_snake_case		:       int,				_snake_case		:       list[int],				_snake_case		:       list[int],				_snake_case		:       int       ):







   if counter == 0 or capacity == 0:
      return 0

   # If weight of the nth item is more than Knapsack of capacity,
   #   then this item cannot be included in the optimal solution,
   # else return the maximum of two cases:
   #   (1) nth item included
   #   (2) not included
   if weights[counter - 1] > capacity:
      return knapsack(_snake_case,				_snake_case,				_snake_case,				counter - 1       )
   else:
      _lowercase							     =       capacity - weights[counter - 1]
      _lowercase							     =       values[counter - 1] + knapsack(
          _snake_case,				_snake_case,				_snake_case,				counter - 1       )
      _lowercase							     =       knapsack(_snake_case,				_snake_case,				_snake_case,				counter - 1       )
      return max(_snake_case,				_snake_case       )


if __name__ == "__main__":
  import doctest

  doctest.testmod()





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()


__lowerCAmelCase						:							Optional[int]			     =						'./tests/fixtures/elise_format0.mid'




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



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





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

							a       =     self.get_dummy_inputs(__lowerCamelCase				)
							a       =     pipe(**__lowerCamelCase				)
							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 				__UpperCAmelCase				( self							:	List[Any]				)   ->	Tuple:
							return super().test_save_load_local()





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





				@skip_mps
				def 				__UpperCAmelCase				( self							:	int				)   ->	Any:
							return super().test_save_load_optional_components()





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





				def 				__UpperCAmelCase				( self							:	Union[str, Any]				)   ->	str:
							pass





				def 				__UpperCAmelCase				( self							:	Tuple				)   ->	Tuple:
							pass





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




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



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





				def 				__UpperCAmelCase				( self							:	Union[str, Any]				)   ->	Any:
							# 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(__lowerCamelCase				)
							pipe.set_progress_bar_config(disable=__lowerCamelCase				)

							def callback(__lowerCamelCase							:	List[str]   , __lowerCamelCase							:	Union[str, Any]				):
										# 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(__lowerCamelCase				)

							a       =     input_tokens[:3]
							a       =     torch.manual_seed(0				)
							pipe(__lowerCamelCase   , num_inference_steps=5   , generator=__lowerCamelCase   , callback=__lowerCamelCase   , output_type="mel"				)





				def 				__UpperCAmelCase				( self							:	Any				)   ->	Union[str, Any]:
							a       =     torch_device

							a       =     SpectrogramDiffusionPipeline.from_pretrained("google/music-spectrogram-diffusion"				)
							a       =     pipe.to(__lowerCamelCase				)
							pipe.set_progress_bar_config(disable=__lowerCamelCase				)
							a       =     MidiProcessor()

							a       =     processor(__lowerCamelCase				)
							# just run two denoising loops
							a       =     input_tokens[:2]

							a       =     torch.manual_seed(0				)
							a       =     pipe(__lowerCamelCase   , num_inference_steps=2   , generator=__lowerCamelCase				)

							a       =     output.audios[0]

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





				def 				__UpperCAmelCase				( self							:	List[str]				)   ->	str:
							a       =     torch_device

							a       =     SpectrogramDiffusionPipeline.from_pretrained("google/music-spectrogram-diffusion"				)
							a       =     pipe.to(__lowerCamelCase				)
							pipe.set_progress_bar_config(disable=__lowerCamelCase				)

							a       =     MidiProcessor()

							a       =     processor(__lowerCamelCase				)

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

							a       =     torch.manual_seed(0				)
							a       =     pipe(__lowerCamelCase   , num_inference_steps=1_00   , generator=__lowerCamelCase				)

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






