





import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


UpperCAmelCase						:       int        =					logging.get_logger(__name__)

UpperCAmelCase						:       List[str]        =					{
    '''facebook/musicgen-small''': '''https://huggingface.co/facebook/musicgen-small/resolve/main/config.json''',
    # See all Musicgen models at https://huggingface.co/models?filter=musicgen
}





class 						_A( snake_case__       ):


				"""simple docstring"""


				UpperCamelCase   :  Dict    =     '''musicgen_decoder'''
				UpperCamelCase   :  Any    =     ['''past_key_values''']



				def __init__(      self					, _A=2048					, _A=2048					, _A=24					, _A=4096					, _A=16					, _A=0.0					, _A=True					, _A="gelu"					, _A=1024					, _A=0.1					, _A=0.0					, _A=0.0					, _A=0.0_2					, _A=False					, _A=4					, _A=2048					, _A=2048					, _A=None					, _A=False					, **_A					, ):
									__A     :						Optional[int]          =    vocab_size
									__A     :						str          =    max_position_embeddings
									__A     :						Any          =    hidden_size
									__A     :						Optional[int]          =    ffn_dim
									__A     :						str          =    num_hidden_layers
									__A     :						Optional[int]          =    num_attention_heads
									__A     :						List[str]          =    dropout
									__A     :						str          =    attention_dropout
									__A     :						Any          =    activation_dropout
									__A     :						Optional[int]          =    activation_function
									__A     :						Dict          =    initializer_factor
									__A     :						int          =    layerdrop
									__A     :						Tuple          =    use_cache
									__A     :						List[Any]          =    scale_embedding  # scale factor will be sqrt(d_model) if True
									__A     :						Optional[int]          =    num_codebooks
									super().__init__(
									    pad_token_id=_A					, bos_token_id=_A					, eos_token_id=_A					, tie_word_embeddings=_A					, **_A					, )





class 						_A( snake_case__       ):


				"""simple docstring"""


				UpperCamelCase   :  List[str]    =     '''musicgen'''
				UpperCamelCase   :  Optional[Any]    =     True



				def __init__(      self					, **_A					):
									super().__init__(**_A					)
									if "text_encoder" not in kwargs or "audio_encoder" not in kwargs or "decoder" not in kwargs:
														raise ValueError('Config has to be initialized with text_encoder, audio_encoder and decoder config'					)

									__A     :						Optional[int]          =    kwargs.pop('text_encoder'					)
									__A     :						int          =    text_encoder_config.pop('model_type'					)

									__A     :						Tuple          =    kwargs.pop('audio_encoder'					)
									__A     :						Dict          =    audio_encoder_config.pop('model_type'					)

									__A     :						Any          =    kwargs.pop('decoder'					)

									__A     :						List[Any]          =    AutoConfig.for_model(_A					, **_A					)
									__A     :						List[str]          =    AutoConfig.for_model(_A					, **_A					)
									__A     :						List[Any]          =    MusicgenDecoderConfig(**_A					)
									__A     :						Any          =    True



				@classmethod
				def  UpperCAmelCase_		(      cls					, _A					, _A					, _A					, **_A					, ):
									return cls(
									    text_encoder=text_encoder_config.to_dict()					, audio_encoder=audio_encoder_config.to_dict()					, decoder=decoder_config.to_dict()					, **_A					, )





				def  UpperCAmelCase_		(      self					):
									__A     :						int          =    copy.deepcopy(self.__dict__					)
									__A     :						List[Any]          =    self.text_encoder.to_dict()
									__A     :						Any          =    self.audio_encoder.to_dict()
									__A     :						int          =    self.decoder.to_dict()
									__A     :						Optional[int]          =    self.__class__.model_type
									return output











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)




