






import argparse

import torch

from transformers import RoFormerConfig, RoFormerForMaskedLM, load_tf_weights_in_roformer
from transformers.utils import logging


logging.set_verbosity_info()


def     _a					(       UpperCamelCase_   :			Tuple      ,				UpperCamelCase_   :			List[str]      ,				UpperCamelCase_   :			List[Any]				)	->				Union[str, Any]:
						"""simple docstring"""







						lowerCAmelCase__	 =  RoFormerConfig.from_json_file(UpperCamelCase_				)
						print(F"Building PyTorch model from configuration: {config}"				)
						lowerCAmelCase__	 =  RoFormerForMaskedLM(UpperCamelCase_				)

						# Load weights from tf checkpoint
						load_tf_weights_in_roformer(UpperCamelCase_      ,				UpperCamelCase_      ,				UpperCamelCase_				)

						# Save pytorch-model
						print(F"Save PyTorch model to {pytorch_dump_path}"				)
						torch.save(model.state_dict()      ,				UpperCamelCase_      ,				_use_new_zipfile_serialization=UpperCamelCase_				)


if __name__ == "__main__":
	a_							=      argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
	)
	parser.add_argument(
	    '''--bert_config_file''',
	    default=None,
	    type=str,
	    required=True,
	    help=(
	        '''The config json file corresponding to the pre-trained BERT model. \n'''
	        '''This specifies the model architecture.'''
	    ),
	)
	parser.add_argument(
	    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
	)
	a_							=      parser.parse_args()
	convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)














import copy

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


a_							=      logging.get_logger(__name__)

a_							=      {
    '''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        lowercase__       (     _UpperCAmelCase		):
	a_             ="""musicgen_decoder"""
	a_             =["""past_key_values"""]

	def __init__(							self						,      __UpperCAmelCase=2048						,      __UpperCAmelCase=2048						,      __UpperCAmelCase=24						,      __UpperCAmelCase=4096						,      __UpperCAmelCase=16						,      __UpperCAmelCase=0.0						,      __UpperCAmelCase=True						,      __UpperCAmelCase="gelu"						,      __UpperCAmelCase=1024						,      __UpperCAmelCase=0.1						,      __UpperCAmelCase=0.0						,      __UpperCAmelCase=0.0						,      __UpperCAmelCase=0.02						,      __UpperCAmelCase=False						,      __UpperCAmelCase=4						,      __UpperCAmelCase=2048						,      __UpperCAmelCase=2048						,      __UpperCAmelCase=None						,      __UpperCAmelCase=False						,      **__UpperCAmelCase						,      )->   Optional[Any]:







							'''simple docstring'''





							lowerCAmelCase__	 =  vocab_size
							lowerCAmelCase__	 =  max_position_embeddings
							lowerCAmelCase__	 =  hidden_size
							lowerCAmelCase__	 =  ffn_dim
							lowerCAmelCase__	 =  num_hidden_layers
							lowerCAmelCase__	 =  num_attention_heads
							lowerCAmelCase__	 =  dropout
							lowerCAmelCase__	 =  attention_dropout
							lowerCAmelCase__	 =  activation_dropout
							lowerCAmelCase__	 =  activation_function
							lowerCAmelCase__	 =  initializer_factor
							lowerCAmelCase__	 =  layerdrop
							lowerCAmelCase__	 =  use_cache
							lowerCAmelCase__	 =  scale_embedding  # scale factor will be sqrt(d_model) if True
							lowerCAmelCase__	 =  num_codebooks
							super().__init__(
							    pad_token_id=__UpperCAmelCase						,      bos_token_id=__UpperCAmelCase						,      eos_token_id=__UpperCAmelCase						,      tie_word_embeddings=__UpperCAmelCase						,      **__UpperCAmelCase						,      )





class        lowercase__       (     _UpperCAmelCase		):
	a_             ="""musicgen"""
	a_             =True

	def __init__(							self						,      **__UpperCAmelCase    )->   Any:







							'''simple docstring'''





							super().__init__(**__UpperCAmelCase    )
							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"    )

							lowerCAmelCase__	 =  kwargs.pop("text_encoder"    )
							lowerCAmelCase__	 =  text_encoder_config.pop("model_type"    )

							lowerCAmelCase__	 =  kwargs.pop("audio_encoder"    )
							lowerCAmelCase__	 =  audio_encoder_config.pop("model_type"    )

							lowerCAmelCase__	 =  kwargs.pop("decoder"    )

							lowerCAmelCase__	 =  AutoConfig.for_model(__UpperCAmelCase						,      **__UpperCAmelCase    )
							lowerCAmelCase__	 =  AutoConfig.for_model(__UpperCAmelCase						,      **__UpperCAmelCase    )
							lowerCAmelCase__	 =  MusicgenDecoderConfig(**__UpperCAmelCase    )
							lowerCAmelCase__	 =  True

	@classmethod
	def 						UpperCAmelCase					(							cls						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase						,      **__UpperCAmelCase						,      )->   Tuple:







							'''simple docstring'''





							return cls(
							    text_encoder=text_encoder_config.to_dict()						,      audio_encoder=audio_encoder_config.to_dict()						,      decoder=decoder_config.to_dict()						,      **__UpperCAmelCase						,      )


	def 						UpperCAmelCase					(							self    )->   Optional[int]:







							'''simple docstring'''





							lowerCAmelCase__	 =  copy.deepcopy(self.__dict__    )
							lowerCAmelCase__	 =  self.text_encoder.to_dict()
							lowerCAmelCase__	 =  self.audio_encoder.to_dict()
							lowerCAmelCase__	 =  self.decoder.to_dict()
							lowerCAmelCase__	 =  self.__class__.model_type
							return output






