




UpperCamelCase__            =     {
    '0000': '0',
    '0001': '1',
    '0010': '2',
    '0011': '3',
    '0100': '4',
    '0101': '5',
    '0110': '6',
    '0111': '7',
    '1000': '8',
    '1001': '9',
    '1010': 'a',
    '1011': 'b',
    '1100': 'c',
    '1101': 'd',
    '1110': 'e',
    '1111': 'f',
}







def       _a (		SCREAMING_SNAKE_CASE_				:							Dict	):
					__lowerCAmelCase							  =			str(__lowercase	).strip()

					# Exceptions
					if not binary_str:
										raise ValueError("Empty string was passed to the function"	)
					__lowerCAmelCase							  =			binary_str[0] == '''-'''
					__lowerCAmelCase							  =			binary_str[1:] if is_negative else binary_str
					if not all(char in "01" for char in binary_str	):
										raise ValueError("Non-binary value was passed to the function"	)

					__lowerCAmelCase							  =			(
					    '''0''' * (4 * (divmod(len(__lowercase	) , 4	)[0] + 1) - len(__lowercase	)) + binary_str
					)

					__lowerCAmelCase							  =			[]
					for x in range(0 , len(__lowercase	) , 4	):
										hexadecimal.append(BITS_TO_HEX[binary_str[x : x + 4]]	)
					__lowerCAmelCase							  =			'''0x''' + ''''''.join(__lowercase	)

					return "-" + hexadecimal_str if is_negative else hexadecimal_str


if __name__ == "__main__":
					from doctest import testmod

					testmod()








import argparse

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


logging.set_verbosity_info()
def   snake_case_    (			__lowercase      ,							__lowercase							):
   # Initialize PyTorch model
   UpperCAmelCase_				:   Optional[Any]							= CanineConfig()
   UpperCAmelCase_				:   Any							= CanineModel(__lowercase							)
   model.eval()

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

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

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

   # Save tokenizer files
   UpperCAmelCase_				:   List[Any]							= CanineTokenizer()
   print(F'''Save tokenizer files to {pytorch_dump_path}'''							)
   tokenizer.save_pretrained(__lowercase							)


if __name__ == "__main__":
       __UpperCamelCase					:	int          =				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					:	str          =				parser.parse_args()
       convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)