





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()


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





@unittest.skip('''The note-seq package currently throws an error on import'''       )
class 						_A( snake_case__    ,			unittest.TestCase       ):


				"""simple docstring"""


				UpperCamelCase   :  Union[str, Any]    =     SpectrogramDiffusionPipeline
				UpperCamelCase   :  Optional[Any]    =     PipelineTesterMixin.required_optional_params - {
				    '''callback''',
				    '''latents''',
				    '''callback_steps''',
				    '''output_type''',
				    '''num_images_per_prompt''',
				}
				UpperCamelCase   :  Dict    =     False
				UpperCamelCase   :  Tuple    =     False
				UpperCamelCase   :  str    =     TOKENS_TO_AUDIO_GENERATION_PARAMS
				UpperCamelCase   :  List[Any]    =     TOKENS_TO_AUDIO_GENERATION_BATCH_PARAMS



				def  UpperCAmelCase_		(      self					):
									torch.manual_seed(0					)
									__A     :						Dict          =    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'					, )

									__A     :						Optional[int]          =    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'					, )

									__A     :						Optional[Any]          =    TaFilmDecoder(
									    input_dims=128					, targets_length=256					, max_decoder_noise_time=2_0_0_0_0.0					, d_model=768					, num_layers=1					, num_heads=1					, d_kv=4					, d_ff=2048					, dropout_rate=0.1					, )

									__A     :						Dict          =    DDPMScheduler()

									__A     :						List[str]          =    {
									    'notes_encoder': notes_encoder.eval(),
									    'continuous_encoder': continuous_encoder.eval(),
									    'decoder': decoder.eval(),
									    'scheduler': scheduler,
									    'melgan': None,
									}
									return components



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

									__A     :						List[str]          =    self.get_dummy_inputs(_A					)
									__A     :						Any          =    pipe(**_A					)
									__A     :						Any          =    output.audios

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

									assert mel_slice.shape == (3, 3)
									__A     :						str          =    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					):
									return super().test_save_load_local()



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



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



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



				def  UpperCAmelCase_		(      self					):
									pass



				def  UpperCAmelCase_		(      self					):
									pass





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





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


				"""simple docstring"""


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



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

									__A     :						Union[str, Any]          =    SpectrogramDiffusionPipeline.from_pretrained('google/music-spectrogram-diffusion'					)
									__A     :						str          =    pipe.melgan
									__A     :						Optional[int]          =    None

									__A     :						Optional[int]          =    pipe.to(_A					)
									pipe.set_progress_bar_config(disable=_A					)

									def callback(_A					, _A					):
														# decode mel to audio
														__A     :						Union[str, Any]          =    melgan(input_features=mel_output.astype(np.floataa					)					)[0]
														assert len(audio[0]					) == 81920 * (step + 1)
														# simulate that audio is played
														return audio

									__A     :						List[str]          =    MidiProcessor()
									__A     :						Any          =    processor(_A					)

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



				def  UpperCAmelCase_		(      self					):
									__A     :						str          =    torch_device

									__A     :						Union[str, Any]          =    SpectrogramDiffusionPipeline.from_pretrained('google/music-spectrogram-diffusion'					)
									__A     :						Optional[int]          =    pipe.to(_A					)
									pipe.set_progress_bar_config(disable=_A					)
									__A     :						Tuple          =    MidiProcessor()

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

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

									__A     :						Any          =    output.audios[0]

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





				def  UpperCAmelCase_		(      self					):
									__A     :						List[Any]          =    torch_device

									__A     :						Tuple          =    SpectrogramDiffusionPipeline.from_pretrained('google/music-spectrogram-diffusion'					)
									__A     :						Union[str, Any]          =    pipe.to(_A					)
									pipe.set_progress_bar_config(disable=_A					)

									__A     :						Union[str, Any]          =    MidiProcessor()

									__A     :						List[str]          =    processor(_A					)

									# just run 4 denoising loops
									__A     :						Optional[int]          =    input_tokens[:4]

									__A     :						List[Any]          =    torch.manual_seed(0					)
									__A     :						List[Any]          =    pipe(_A					, num_inference_steps=100					, generator=_A					)

									__A     :						Union[str, Any]          =    output.audios[0]
									assert abs(np.abs(_A					).sum() - 9_3_8_9.1_1_1_1					) < 5e-2











import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
UpperCAmelCase						:       Tuple        =					logging.get_logger(__name__)

UpperCAmelCase						:       List[Any]        =					['''UtteranceLevel''']







@torch.no_grad()
def 			_SCREAMING_SNAKE_CASE    (       a ,						a ,						a ,						a  )				->				List[str]:
					__A     :						Any          =    torch.load(a ,						map_location='cpu'  )
					if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
										raise NotImplementedError(F"""The supported s3prl models are {SUPPORTED_MODELS}"""  )

					__A     :						Dict          =    checkpoint['Downstream']

					__A     :						Optional[Any]          =    HubertConfig.from_pretrained(a  )
					__A     :						Tuple          =    HubertForSequenceClassification.from_pretrained(a ,						config=a  )
					__A     :						str          =    WavaVecaFeatureExtractor.from_pretrained(
					    a ,						return_attention_mask=a ,						do_normalize=a  )

					if hf_congfig.use_weighted_layer_sum:
										__A     :						Optional[Any]          =    checkpoint['Featurizer']['weights']

					__A     :						Dict          =    downstream_dict['projector.weight']
					__A     :						int          =    downstream_dict['projector.bias']
					__A     :						List[Any]          =    downstream_dict['model.post_net.linear.weight']
					__A     :						int          =    downstream_dict['model.post_net.linear.bias']

					hf_feature_extractor.save_pretrained(a  )
					hf_model.save_pretrained(a  )


if __name__ == "__main__":
				UpperCAmelCase						:       Dict        =					argparse.ArgumentParser()
				parser.add_argument(
				    '''--base_model_name''', default=None, type=str, help='''Name of the huggingface pretrained base model.'''
				)
				parser.add_argument('''--config_path''', default=None, type=str, help='''Path to the huggingface classifier config.''')
				parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to the s3prl checkpoint.''')
				parser.add_argument('''--model_dump_path''', default=None, type=str, help='''Path to the final converted model.''')
				UpperCAmelCase						:       Dict        =					parser.parse_args()
				convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)




