code: 
  
  """simple docstring"""
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  
  
  _A						=     logging.get_logger(__name__)
  
  _A						=     {
      """ZinengTang/tvlt-base""": """https://huggingface.co/ZinengTang/tvlt-base/blob/main/config.json""",
  }
  
  class 			lowerCamelCase  (					lowerCAmelCase__      ):
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    SCREAMING_SNAKE_CASE							=     'tvlt'
  
  
  
    def __init__(self       ,					_lowerCamelCase=224       ,					_lowerCamelCase=2048       ,					_lowerCamelCase=128       ,					_lowerCamelCase=[16, 16]       ,					_lowerCamelCase=[16, 16]       ,					_lowerCamelCase=3       ,					_lowerCamelCase=1       ,					_lowerCamelCase=8       ,					_lowerCamelCase=768       ,					_lowerCamelCase=12       ,					_lowerCamelCase=12       ,					_lowerCamelCase=3072       ,					_lowerCamelCase="gelu"       ,					_lowerCamelCase=0.0       ,					_lowerCamelCase=0.0       ,					_lowerCamelCase=0.02       ,					_lowerCamelCase=1e-6       ,					_lowerCamelCase=True       ,					_lowerCamelCase=False       ,					_lowerCamelCase=16       ,					_lowerCamelCase=512       ,					_lowerCamelCase=8       ,					_lowerCamelCase=2048       ,					_lowerCamelCase=0.75       ,					_lowerCamelCase=0.15       ,					_lowerCamelCase="frame-level"       ,					_lowerCamelCase=True       ,					_lowerCamelCase=True       ,					_lowerCamelCase="classification"       ,					**_lowerCamelCase       ,					):
  
  
          """simple docstring"""
  
          super().__init__(**_lowerCamelCase  )
  
          if audio_mask_type not in ("frame-level", "patch_level"):
                raise ValueError(
                    """audio_mask_type must be one of two acceptable strategies - {'frame_level', 'patch-level') """
                    F"""got {audio_mask_type}"""  )
  
          UpperCAmelCase__ :       int						=						image_size
          UpperCAmelCase__ :       List[str]						=						spectrogram_length
          UpperCAmelCase__ :       str						=						frequency_length
          UpperCAmelCase__ :       Optional[Any]						=						image_patch_size
          UpperCAmelCase__ :       Optional[Any]						=						audio_patch_size
          UpperCAmelCase__ :       List[str]						=						num_image_channels
          UpperCAmelCase__ :       Tuple						=						num_audio_channels
          UpperCAmelCase__ :       List[Any]						=						num_frames
  
          UpperCAmelCase__ :       Dict						=						hidden_size
          UpperCAmelCase__ :       Tuple						=						num_hidden_layers
          UpperCAmelCase__ :       List[Any]						=						num_attention_heads
          UpperCAmelCase__ :       Any						=						intermediate_size
          UpperCAmelCase__ :       Any						=						hidden_act
          UpperCAmelCase__ :       Dict						=						hidden_dropout_prob
          UpperCAmelCase__ :       Optional[Any]						=						attention_probs_dropout_prob
          UpperCAmelCase__ :       Optional[int]						=						initializer_range
          UpperCAmelCase__ :       int						=						layer_norm_eps
          UpperCAmelCase__ :       Optional[int]						=						qkv_bias
          UpperCAmelCase__ :       int						=						use_mean_pooling
  
          UpperCAmelCase__ :       int						=						decoder_num_attention_heads
          UpperCAmelCase__ :       int						=						decoder_hidden_size
          UpperCAmelCase__ :       int						=						decoder_num_hidden_layers
          UpperCAmelCase__ :       Dict						=						decoder_intermediate_size
          UpperCAmelCase__ :       int						=						pixel_mask_ratio
          UpperCAmelCase__ :       Optional[Any]						=						audio_mask_ratio
          UpperCAmelCase__ :       Union[str, Any]						=						audio_mask_type
  
          UpperCAmelCase__ :       Tuple						=						task_matching
          UpperCAmelCase__ :       str						=						task_mae
          UpperCAmelCase__ :       str						=						loss_type
  
  
style_context: 
  
  """simple docstring"""
  import argparse
  import json
  
  from transformers import GPTNeoConfig, GPTNeoForCausalLM, load_tf_weights_in_gpt_neo
  from transformers.utils import logging
  
  
  logging.set_verbosity_info()
  
  
  
  
  def 				a__	(	lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase						)  ->   Dict:
        # Initialise PyTorch model
        UpperCAmelCase__ :       Union[str, Any]						=						json.load(open(lowerCAmelCase ,  """r"""						)						)
        UpperCAmelCase__ :       Any						=						GPTNeoConfig(
            hidden_size=config_json["""n_embd"""] ,  num_layers=config_json["""n_layer"""] ,  num_heads=config_json["""n_head"""] ,  attention_types=config_json["""attention_types"""] ,  max_position_embeddings=config_json["""n_positions"""] ,  resid_dropout=config_json["""res_dropout"""] ,  embed_dropout=config_json["""embed_dropout"""] ,  attention_dropout=config_json["""attn_dropout"""] ,  )
        print(F"""Building PyTorch model from configuration: {config}"""						)
        UpperCAmelCase__ :       Dict						=						GPTNeoForCausalLM(lowerCAmelCase						)
  
        # Load weights from tf checkpoint
        load_tf_weights_in_gpt_neo(lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase						)
  
        # Save pytorch-model
        print(F"""Save PyTorch model to {pytorch_dump_path}"""						)
        model.save_pretrained(lowerCAmelCase						)
  
  
  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(
          """--config_file""",
          default=None,
          type=str,
          required=True,
          help=(
              """The config json file corresponding to the pre-trained mesh-tf 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.config_file, args.pytorch_dump_path)
  
  