




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


SCREAMING_SNAKE_CASE__       :      Optional[int]  =   logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__       :      Union[str, Any]  =   {
    'RWKV/rwkv-4-169m-pile': 'https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json',
    'RWKV/rwkv-4-430m-pile': 'https://huggingface.co/RWKV/rwkv-4-430m-pile/resolve/main/config.json',
    'RWKV/rwkv-4-1b5-pile': 'https://huggingface.co/RWKV/rwkv-4-1b5-pile/resolve/main/config.json',
    'RWKV/rwkv-4-3b-pile': 'https://huggingface.co/RWKV/rwkv-4-3b-pile/resolve/main/config.json',
    'RWKV/rwkv-4-7b-pile': 'https://huggingface.co/RWKV/rwkv-4-7b-pile/resolve/main/config.json',
    'RWKV/rwkv-4-14b-pile': 'https://huggingface.co/RWKV/rwkv-4-14b-pile/resolve/main/config.json',
    'RWKV/rwkv-raven-1b5': 'https://huggingface.co/RWKV/rwkv-raven-1b5/resolve/main/config.json',
    'RWKV/rwkv-raven-3b': 'https://huggingface.co/RWKV/rwkv-raven-3b/resolve/main/config.json',
    'RWKV/rwkv-raven-7b': 'https://huggingface.co/RWKV/rwkv-raven-7b/resolve/main/config.json',
    'RWKV/rwkv-raven-14b': 'https://huggingface.co/RWKV/rwkv-raven-14b/resolve/main/config.json',
}




class 		UpperCamelCase__	(lowerCAmelCase__     ):

    '''simple docstring'''



    lowerCamelCase_      :	Dict	     =      """rwkv"""
    lowerCamelCase_      :	List[Any]	     =      {"""max_position_embeddings""": """context_length"""}







    def __init__(    self     ,  UpperCamelCase__=5_0277     ,  UpperCamelCase__=1024     ,  UpperCamelCase__=4096     ,  UpperCamelCase__=32     ,  UpperCamelCase__=None     ,  UpperCamelCase__=None     ,  UpperCamelCase__=1e-5     ,  UpperCamelCase__=0     ,  UpperCamelCase__=0     ,  UpperCamelCase__=6     ,  UpperCamelCase__=False     ,  UpperCamelCase__=True     ,  **UpperCamelCase__     ,  )     ->      Union[str, Any]:
           lowerCamelCase   :      Optional[int]			 =					vocab_size
           lowerCamelCase   :      Tuple			 =					context_length
           lowerCamelCase   :      Dict			 =					hidden_size
           lowerCamelCase   :      Optional[int]			 =					num_hidden_layers
           lowerCamelCase   :      List[str]			 =					attention_hidden_size if attention_hidden_size is not None else hidden_size
           lowerCamelCase   :      Union[str, Any]			 =					intermediate_size if intermediate_size is not None else 4 * hidden_size
           lowerCamelCase   :      Tuple			 =					layer_norm_epsilon
           lowerCamelCase   :      int			 =					rescale_every
           lowerCamelCase   :      List[str]			 =					use_cache

           lowerCamelCase   :      Optional[int]			 =					bos_token_id
           lowerCamelCase   :      Optional[int]			 =					eos_token_id

           super().__init__(
               tie_word_embeddings=UpperCamelCase__     ,  bos_token_id=UpperCamelCase__     ,  eos_token_id=UpperCamelCase__     ,  **UpperCamelCase__     )












import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()

def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )	-> Dict:
       # Initialise PyTorch model
       lowerCamelCase   :      Any			 =					TaConfig.from_json_file(_SCREAMING_SNAKE_CASE    )
       print(f'''Building PyTorch model from configuration: {config}'''    )
       lowerCamelCase   :      str			 =					TaForConditionalGeneration(_SCREAMING_SNAKE_CASE    )

       # Load weights from tf checkpoint
       load_tf_weights_in_ta(_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__":
       SCREAMING_SNAKE_CASE__       :      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 T5 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.'
       )
       SCREAMING_SNAKE_CASE__       :      str  =   parser.parse_args()
       convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)






