




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


UpperCamelCase								=     logging.get_logger(__name__)

UpperCamelCase								=     {
    'bert-base-uncased': 'https://huggingface.co/bert-base-uncased/resolve/main/config.json',
    # QDQBERT models can be loaded from any BERT checkpoint, available at https://huggingface.co/models?filter=bert
}





class  __lowerCamelCase       (    UpperCamelCase__    ):




    """simple docstring"""

    snake_case__					=      "qdqbert"


    def __init__(       self     :     Any   ,	SCREAMING_SNAKE_CASE__     :     str=30_522   ,	SCREAMING_SNAKE_CASE__     :     str=768   ,	SCREAMING_SNAKE_CASE__     :     Union[str, Any]=12   ,	SCREAMING_SNAKE_CASE__     :     int=12   ,	SCREAMING_SNAKE_CASE__     :     Any=3_072   ,	SCREAMING_SNAKE_CASE__     :     Dict="gelu"   ,	SCREAMING_SNAKE_CASE__     :     Dict=0.1   ,	SCREAMING_SNAKE_CASE__     :     Optional[Any]=0.1   ,	SCREAMING_SNAKE_CASE__     :     List[Any]=512   ,	SCREAMING_SNAKE_CASE__     :     Optional[int]=2   ,	SCREAMING_SNAKE_CASE__     :     List[Any]=0.02   ,	SCREAMING_SNAKE_CASE__     :     Any=1e-1_2   ,	SCREAMING_SNAKE_CASE__     :     Union[str, Any]=True   ,	SCREAMING_SNAKE_CASE__     :     Any=1   ,	SCREAMING_SNAKE_CASE__     :     Optional[Any]=0   ,	SCREAMING_SNAKE_CASE__     :     Union[str, Any]=2   ,	**SCREAMING_SNAKE_CASE__     :     Any   ,	)      ->       Optional[Any]:
         super().__init__(pad_token_id=SCREAMING_SNAKE_CASE__   ,	bos_token_id=SCREAMING_SNAKE_CASE__   ,	eos_token_id=SCREAMING_SNAKE_CASE__   ,	**SCREAMING_SNAKE_CASE__   )

         lowerCAmelCase__		= vocab_size
         lowerCAmelCase__		= max_position_embeddings
         lowerCAmelCase__		= hidden_size
         lowerCAmelCase__		= num_hidden_layers
         lowerCAmelCase__		= num_attention_heads
         lowerCAmelCase__		= intermediate_size
         lowerCAmelCase__		= hidden_act
         lowerCAmelCase__		= hidden_dropout_prob
         lowerCAmelCase__		= attention_probs_dropout_prob
         lowerCAmelCase__		= initializer_range
         lowerCAmelCase__		= type_vocab_size
         lowerCAmelCase__		= layer_norm_eps
         lowerCAmelCase__		= use_cache






import argparse

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


logging.set_verbosity_info()



def 							_A   (						lowerCAmelCase_     :					Optional[Any]   ,				lowerCAmelCase_     :					Tuple			):
     """simple docstring"""

     lowerCAmelCase__		= CanineConfig()
     lowerCAmelCase__		= CanineModel(lowerCAmelCase_			)
     model.eval()

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

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

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

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


if __name__ == "__main__":
     UpperCamelCase								=     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.',
     )
     UpperCamelCase								=     parser.parse_args()
     convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)
