
import argparse

from transformers import CanineConfig, CanineModel, CanineTokenizer, load_tf_weights_in_canine
from transformers.utils import logging


logging.set_verbosity_info()



def 		lowercase_     (  A__					,						A__						)				->				Optional[Any]:
    """simple docstring"""

    snake_case						=   CanineConfig()
    snake_case						=   CanineModel(A__						)
    model.eval()

    print(F'Building PyTorch model from configuration: {config}'						)

    # Load weights from tf checkpoint
    load_tf_weights_in_canine(A__					,						A__					,						A__						)

    # Save pytorch-model (weights and configuration)
    print(F'Save PyTorch model to {pytorch_dump_path}'						)
    model.save_pretrained(A__						)

    # Save tokenizer files
    snake_case						=   CanineTokenizer()
    print(F'Save tokenizer files to {pytorch_dump_path}'						)
    tokenizer.save_pretrained(A__						)


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. Should end with model.ckpt",
   )
   parser.add_argument(
       "--pytorch_dump_path",
       default=None,
       type=str,
       required=True,
       help="Path to a folder where the PyTorch model will be placed.",
   )
   _A       =					parser.parse_args()
   convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)



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


_A       =					logging.get_logger(__name__)

_A       =					{
    "openbmb/cpm-ant-10b": "https://huggingface.co/openbmb/cpm-ant-10b/blob/main/config.json"
    # See all CPMAnt models at https://huggingface.co/models?filter=cpmant
}







class 	lowerCamelCase						(    A_    ):
     UpperCAmelCase__							:				List[Any] 	=						"cpmant"





     def __init__(self :       List[Any]							,       _A :       int = 3_0_7_2_0							,       _A :       int = 4_0_9_6							,       _A :       int = 3_2							,       _A :       int = 1_2_8							,       _A :       int = 1_0_2_4_0							,       _A :       int = 4_8							,       _A :       int = 0.0							,       _A :       int = 5_1_2							,       _A :       int = 2_0_4_8							,       _A :       int = 1E-6							,       _A :       float = 1.0							,       _A :       int = 3_2							,       _A :       int = 3_2							,       _A :       int = 3_2							,       _A :       bool = True							,       **_A :       Dict							,       )		->		Dict:
         super().__init__(**_A  )
         snake_case						=   prompt_types
         snake_case						=   prompt_length
         snake_case						=   segment_types
         snake_case						=   hidden_size
         snake_case						=   num_attention_heads
         snake_case						=   dim_head
         snake_case						=   dim_ff
         snake_case						=   num_hidden_layers
         snake_case						=   position_bias_num_buckets
         snake_case						=   position_bias_max_distance
         snake_case						=   dropout_p
         snake_case						=   eps
         snake_case						=   use_cache
         snake_case						=   vocab_size
         snake_case						=   init_std

