






"""simple docstring"""





from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase					=     logging.get_logger(__name__)

UpperCamelCase					=     {
    """ZinengTang/tvlt-base""": """https://huggingface.co/ZinengTang/tvlt-base/blob/main/config.json""",
}







class 						_a  (		lowercase_       ):





   '''simple docstring'''






   UpperCamelCase__     	=							"""tvlt"""





   def __init__(       self					, UpperCAmelCase_=224					, UpperCAmelCase_=2_048					, UpperCAmelCase_=128					, UpperCAmelCase_=[16, 16]					, UpperCAmelCase_=[16, 16]					, UpperCAmelCase_=3					, UpperCAmelCase_=1					, UpperCAmelCase_=8					, UpperCAmelCase_=768					, UpperCAmelCase_=12					, UpperCAmelCase_=12					, UpperCAmelCase_=3_072					, UpperCAmelCase_="gelu"					, UpperCAmelCase_=0.0					, UpperCAmelCase_=0.0					, UpperCAmelCase_=0.02					, UpperCAmelCase_=1E-6					, UpperCAmelCase_=True					, UpperCAmelCase_=False					, UpperCAmelCase_=16					, UpperCAmelCase_=512					, UpperCAmelCase_=8					, UpperCAmelCase_=2_048					, UpperCAmelCase_=0.75					, UpperCAmelCase_=0.15					, UpperCAmelCase_="frame-level"					, UpperCAmelCase_=True					, UpperCAmelCase_=True					, UpperCAmelCase_="classification"					, **UpperCAmelCase_					, )			->   Tuple:




          '''simple docstring'''




          super().__init__(**UpperCAmelCase_)

          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}""")

          lowercase__:							Optional[int]         =     image_size
          lowercase__:							Dict         =     spectrogram_length
          lowercase__:							List[str]         =     frequency_length
          lowercase__:							int         =     image_patch_size
          lowercase__:							Optional[int]         =     audio_patch_size
          lowercase__:							Tuple         =     num_image_channels
          lowercase__:							List[str]         =     num_audio_channels
          lowercase__:							Tuple         =     num_frames

          lowercase__:							List[str]         =     hidden_size
          lowercase__:							str         =     num_hidden_layers
          lowercase__:							List[str]         =     num_attention_heads
          lowercase__:							List[Any]         =     intermediate_size
          lowercase__:							Union[str, Any]         =     hidden_act
          lowercase__:							Optional[int]         =     hidden_dropout_prob
          lowercase__:							Optional[Any]         =     attention_probs_dropout_prob
          lowercase__:							Union[str, Any]         =     initializer_range
          lowercase__:							Any         =     layer_norm_eps
          lowercase__:							Any         =     qkv_bias
          lowercase__:							List[str]         =     use_mean_pooling

          lowercase__:							Tuple         =     decoder_num_attention_heads
          lowercase__:							Any         =     decoder_hidden_size
          lowercase__:							Any         =     decoder_num_hidden_layers
          lowercase__:							Union[str, Any]         =     decoder_intermediate_size
          lowercase__:							Optional[Any]         =     pixel_mask_ratio
          lowercase__:							str         =     audio_mask_ratio
          lowercase__:							Dict         =     audio_mask_type

          lowercase__:							Union[str, Any]         =     task_matching
          lowercase__:							Union[str, Any]         =     task_mae
          lowercase__:							Union[str, Any]         =     loss_type










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 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   List[Any]     ,	SCREAMING_SNAKE_CASE__     :   Union[str, Any]     ,	SCREAMING_SNAKE_CASE__     :   Any							):
     # Initialise PyTorch model
     __UpperCamelCase       				=json.load(open(SCREAMING_SNAKE_CASE__     ,	'r'							)							)
     __UpperCamelCase       				=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       				=GPTNeoForCausalLM(SCREAMING_SNAKE_CASE__							)

     # Load weights from tf checkpoint
     load_tf_weights_in_gpt_neo(SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__							)

     # Save pytorch-model
     print(F'Save PyTorch model to {pytorch_dump_path}'							)
     model.save_pretrained(SCREAMING_SNAKE_CASE__							)


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)





