'''simple docstring'''
import copy

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


_A		:			str	      =logging.get_logger(__name__)

_A		:			Any	      ={
    '''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 _lowerCAmelCase (  snake_case				):
      UpperCAmelCase_  :	Any     =					"musicgen_decoder"
      UpperCAmelCase_  :	Any     =					["past_key_values"]







      def __init__(     self     ,						lowercase=2048     ,						lowercase=2048     ,						lowercase=24     ,						lowercase=4096     ,						lowercase=16     ,						lowercase=0.0     ,						lowercase=True     ,						lowercase="gelu"     ,						lowercase=1024     ,						lowercase=0.1     ,						lowercase=0.0     ,						lowercase=0.0     ,						lowercase=0.02     ,						lowercase=False     ,						lowercase=4     ,						lowercase=2048     ,						lowercase=2048     ,						lowercase=None     ,						lowercase=False     ,						**lowercase     ,						):
        lowercase							:       Union[str, Any]											=      vocab_size
        lowercase							:       Tuple											=      max_position_embeddings
        lowercase							:       List[str]											=      hidden_size
        lowercase							:       Optional[Any]											=      ffn_dim
        lowercase							:       Tuple											=      num_hidden_layers
        lowercase							:       List[Any]											=      num_attention_heads
        lowercase							:       Dict											=      dropout
        lowercase							:       Any											=      attention_dropout
        lowercase							:       List[Any]											=      activation_dropout
        lowercase							:       Tuple											=      activation_function
        lowercase							:       int											=      initializer_factor
        lowercase							:       Tuple											=      layerdrop
        lowercase							:       List[str]											=      use_cache
        lowercase							:       Tuple											=      scale_embedding  # scale factor will be sqrt(d_model) if True
        lowercase							:       Union[str, Any]											=      num_codebooks
        super().__init__(
            pad_token_id=lowercase     ,						bos_token_id=lowercase     ,						eos_token_id=lowercase     ,						tie_word_embeddings=lowercase     ,						**lowercase     ,						)





class _lowerCAmelCase (  snake_case				):
      UpperCAmelCase_  :	Dict     =					"musicgen"
      UpperCAmelCase_  :	List[str]     =					True







      def __init__(     self     ,						**lowercase    ):
        super().__init__(**lowercase    )
        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'''    )

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

        lowercase							:       int											=      kwargs.pop('''audio_encoder'''    )
        lowercase							:       Tuple											=      audio_encoder_config.pop('''model_type'''    )

        lowercase							:       Union[str, Any]											=      kwargs.pop('''decoder'''    )

        lowercase							:       Tuple											=      AutoConfig.for_model(lowercase     ,						**lowercase    )
        lowercase							:       Optional[int]											=      AutoConfig.for_model(lowercase     ,						**lowercase    )
        lowercase							:       Dict											=      MusicgenDecoderConfig(**lowercase    )
        lowercase							:       List[str]											=      True







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







      def     lowerCamelCase__			(     self    ):
        lowercase							:       Optional[int]											=      copy.deepcopy(self.__dict__    )
        lowercase							:       List[Any]											=      self.text_encoder.to_dict()
        lowercase							:       List[Any]											=      self.audio_encoder.to_dict()
        lowercase							:       Optional[Any]											=      self.decoder.to_dict()
        lowercase							:       Any											=      self.__class__.model_type
        return output
'''simple docstring'''
import argparse

import torch

from transformers import ElectraConfig, ElectraForMaskedLM, ElectraForPreTraining, load_tf_weights_in_electra
from transformers.utils import logging


logging.set_verbosity_info()







def 							__UpperCamelCase   (				__UpperCAmelCase     :				Optional[Any]   ,      __UpperCAmelCase     :				Tuple   ,      __UpperCAmelCase     :				Union[str, Any]   ,      __UpperCAmelCase     :				List[str] )       ->  List[str]:
  # Initialise PyTorch model
  lowercase							:       Any											=      ElectraConfig.from_json_file(__UpperCAmelCase )
  print(f"""Building PyTorch model from configuration: {config}""" )

  if discriminator_or_generator == "discriminator":
    lowercase							:       Union[str, Any]											=      ElectraForPreTraining(__UpperCAmelCase )
  elif discriminator_or_generator == "generator":
    lowercase							:       Tuple											=      ElectraForMaskedLM(__UpperCAmelCase )
  else:
    raise ValueError('''The discriminator_or_generator argument should be either \'discriminator\' or \'generator\'''' )

  # Load weights from tf checkpoint
  load_tf_weights_in_electra(
      __UpperCAmelCase   ,      __UpperCAmelCase   ,      __UpperCAmelCase   ,      discriminator_or_generator=__UpperCAmelCase )

  # Save pytorch-model
  print(f"""Save PyTorch model to {pytorch_dump_path}""" )
  torch.save(model.state_dict()   ,      __UpperCAmelCase )


if __name__ == "__main__":
 _A		:			str	      =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(
     '''--config_file''',
     default=None,
     type=str,
     required=True,
     help='''The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.''',
 )
 parser.add_argument(
     '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
 )
 parser.add_argument(
     '''--discriminator_or_generator''',
     default=None,
     type=str,
     required=True,
     help=(
         '''Whether to export the generator or the discriminator. Should be a string, either \'discriminator\' or '''
         '''\'generator\'.'''
     ),
 )
 _A		:			Any	      =parser.parse_args()
 convert_tf_checkpoint_to_pytorch(
     args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.discriminator_or_generator
 )